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Aggregate-binder adhesiveness impacts asphalt mixture performance. Evaluation of this interface within mixes typically involves visual assessment and digital image processing (DIP), often resulting in an average value for this complex property. Current computational simulations overlook the possibility of incomplete aggregate adhesion to the binder, a common occurrence in cases of adhesive/cohesive failure between materials. This study proposes an interface index determined from adhesiveness test results in order to improve computational simulations. For this purpose, twelve adhesiveness tests, following Brazilian standards, including mineral and steel-slag aggregates, and neat and modified binders, were conducted. Software with a graphical user interface (GUI) was developed to quantify the percentage of aggregate area coated by binder (%Coated) post-testing. Descriptive statistics and probability distributions were applied to %Coated values, in addition to the quality of measurements using DIP. The DIP values exhibited sensitivity across all examined covering scenarios, with statistical tests suggesting a high likelihood of accurately representing the %Coated parameter from the assessed probability distributions. In conclusion, the developed software enables satisfactory quantification of %Coated. Furthermore, this study indicates that the adhesion test results must be represented considering the data variability, with the Beta distribution being a good way to represent the behavior. • The Beta distribution of probability represents the result of the aggregate-binder adhesiveness test. • 100 particles are a reasonable number of observations to represent the Brazilian adhesiveness test. • The adhesion test can be represented with a mean value if the samples are close to 100 or 0 % of the coated aggregate area. • Steel slag aggregates, when compared to crushed mineral aggregates, demonstrated more uniform adhesion. • The binder brightness significantly influences the digital image processing with only samples with modified binders. [ABSTRACT FROM AUTHOR]

The adhesion performance between asphalt and aggregates is one of the key parameters to ensure the water resistance of asphalt mixtures. The traditional method is using the water boiling method to evaluate the adhesion between asphalt and aggregates. Because of the strong adhesion between SBS asphalt and aggregates, removing SBS asphalt from the surface of aggregates using the water boiling method to assess the adhesion between various types of rocks and SBS asphalt is challenging. Hence, this paper proposed rolling bottle test and image processing to assess the adhesion property between SBS asphalt and aggregates. Eight types of rock aggregates and SBS modified asphalt (virgin and long term aging) were selected for rolling bottle test. A method for calculating asphalt peeling rate on aggregate surface based on image processing was proposed. A computing program, pr-cal , was developed and verified with area calculation program. The result shows that the error is around 10%, which indicates that the peeling rate calculation program is relatively accurate. The results of the peeling rate data indicates that limestone has the better adhesion, granite has the worst adhesion, and the adhesion of other rock types aggregates(basalt, diabase, tuff) is in the middle. The adhesion of the same rock type aggregates from different sources is also different, and asphalt aging will reduce its adhesion performance with aggregates. • A method for calculating asphalt peeling rate based on image processing was proposed. • A computing program, pr-cal , was developed and verified with area calculation program. • asphalt aging will reduce its adhesion performance with aggregates. [ABSTRACT FROM AUTHOR]

In the fracture-based healing test, asphalt specimens generally break into two halves and the aggregates in the crack path also break. In this study, the effect of aggregate damage on the self-healing performance of asphalt concrete was studied. To achieve this, semi-circle asphalt specimens were produced. Then, they were broken using the Semi-circle bending test (SCB) at different temperatures (i.e., −20, −10, 0, 10, and 20 °C) to create various aggregate damage situations. Next, the fracture surfaces of specimens were photographed and the percentages of damage types (i.e., adhesive, cohesive, and broken aggregates) were obtained by using image processing techniques. After the specimens were healed at a constant 55 °C, the SCB test was applied again to measure the strength of the specimens and to calculate healing performance. Finally, fracture surfaces were photographed again. According to findings, as breaking temperatures increase, broken aggregates and adhesive damage decrease. Cohesive damage, on the other hand, rose with the high breaking temperature. Healing performance was better at low breaking temperatures. A positive correlation was found between aggregate damage and healing levels, but it was explained as a spurious relationship. The real correlation was negative which was obtained from a second dataset. • An image processing method was used to obtain broken aggregate percent in fracture surfaces. • Various aggregate damage percentages were created by using different breaking temperatures. • The drawback of using different breaking temperatures was explained. • Healing performance was better at low breaking temperatures. • A negative correlation was determined between aggregate damage and healing levels with an additional dataset. [ABSTRACT FROM AUTHOR]

This research aims to assess the microscopic differences in void characteristics of porous asphalt mixtures and their effect on mechanical and functional performance when prepared using different compaction methods. Marshall compaction (MC) and Superpave Gyratory Compactor (SGC) methods were employed to prepare the specimens, and digital image processing techniques were used to analyze the differences in void size, shape and complexity of the mixtures. Through bending, fatigue, drainage and noise reduction tests, the mechanical and functional performances of the mixture specimens under two compaction methods were compared and analyzed. The results revealed that the compaction method had a substantial effect on the specimens' drainage and noise reduction performance. Moreover, the correlation results between the meso-structure and macro performances of the mixtures indicate that SGC exhibits greater responsiveness to the overall performance of the mixtures. These findings facilitate the understanding of the compaction mechanism, thereby improving the design and compaction procedures, and providing insights into the relationship between microstructure and macroscopic performance of asphalt mixtures. • The effects of compaction methods on void characteristics and macro performances. • Analyzed the microstructural differences in void characteristics during compaction. • The SGC method strongly correlates with mixture's mechanics, while MC with functionality. • Established scale correlation between compaction methods, void parameters, and mixture performances. [ABSTRACT FROM AUTHOR]

A novel approach utilizing in-situ Computed Tomography (CT) scanning and digital image processing techniques was developed to overcome the limitations of traditional CT image analysis in studying microcrack evolution in asphalt mixtures. Two types of graded asphalt mixtures (continuous gradation/AC and intermittent gradation/SMA) were scanned in-situ to capture internal structural changes. Digital image processing distinguished primary voids from cracks and extracted crack evolution. Multivariate regression analysis linked crack parameters to compressive strain. The study found that crack evolution during uniaxial compression in asphalt mixtures can be divided into densification, linear elastic deformation, and nonlinear failure stages. As load increases, the number, volume, and void percentage occupied by cracks rise, peak, and then decrease due to merging and engulfing after the ultimate load. The skeletal structure of cracks was obtained, and a damage evolution equation was established, linking crack parameters to damage evolution. Crack tortuosity was used to characterize the resistance effect of different gradations on crack propagation. This methodology provides new insights and technical support for studying damage evolution and understanding the failure mechanisms of asphalt mixtures. • Use in-situ CT technology to scan the uniaxial compression process of asphalt mixture. • Combine digital image processing technology and three-dimensional shape coefficients to distinguish internal voids and cracks in the specimen. • Quantitatively describe the evolution process of cracks under loading. • Establish a damage evolution equation between the damage variable and the vertical strain. [ABSTRACT FROM AUTHOR]

The distribution characteristics of asphalt and cement mortar play a crucial role in determining the cracking resistance of semi-flexible pavements (SFP). In this study, digital image processing technology was utilized to quantify the thickness distribution of asphalt and cement mortar within SFP, and a parallel line segmentation method was employed to characterize the random distribution of mortar thickness (RDMT) of asphalt and cement mortar. Variance, coefficient of variation (COV), and interquartile range were proposed to characterize RDMT, examining the effects of air voids (AV) content, nominal maximum aggregate size (NMAS), and compaction method on RDMT, and its variation along the specimen height. Semi-circular bending test was conducted to evaluated the cracking resistance of SFP, exploring the relationship between RDMT and cracking resistance. Results indicate that parallel line segmentation method with 36 parts and 25 parts is recommended to characterize the RDMT of asphalt and cement mortar, respectively. An increase in AV content led to a higher RDMT for asphalt mortar but a lower RDMT for cement mortar. Conversely, as NMAS increased, the average thickness of asphalt and cement mortar increased, while the RDMT of asphalt mortar decreased and that of cement mortar increased. RDMT of asphalt and cement mortar is higher under Superpave gyration compaction method compared to the Marshall compaction method. Additionally, RDMT increased from the top to the bottom of the specimen height. Based on the RDMT, a random distribution index (RDI) was proposed, which exhibited a strong linear relationship with the cracking resistance of SFP. When the RDI decreased by 44.4 %, the corresponding cracking resistance of SFP increased by 84.0 %. Improving the distribution of asphalt and cement mortar could enhance the cracking resistance of SFP. • RDMT of asphalt and cement mortar in semi-flexible pavement was characterized. • Effects of AV content, NMAS, and compaction method on the RDMT of asphalt and cement mortar were investigated. • Relationships between cracking resistance of SFP and RDMT of asphalt and cement mortar were firstly investigated. [ABSTRACT FROM AUTHOR]

The article concerned on particle morphology of mineral filler and its impact on mastic cracking property at low temperatures. Limestone and granite fillers with four-sized groups were prepared and further investigated using a simplex-centroid experimental design. The particle morphologies of each filler were captured using a digital image processing. By applying bending beam rheometer tests, mastic cracking behavior was investigated at low temperatures. Results show that compared with granite filler, each limestone filler shows less regular contour shape (except l 1), less abundant angularity (except l 1 , l 2 , l 5 and l 17) and surface texture when performed by a simplex-centroid experimental design. The established fractional derivative Burgers model considering creep damage can effectively describe mastic cracking behavior at low temperatures. Composite fractal dimension is the decisively relevant factor with mastic relaxation behavior. Moreover, mastic creep damage is obviously affected by composite morphology characteristics and filler lithology with increasing loading time. All the results are instructive to inform researches of the selection of mineral filler with appropriate particle morphology that will produce asphalt materials with desired performance. [Display omitted] • Simplex-centroid experimental design was used for filler size fraction composition. • Cracking behavior of mastics with various particle morphologies was elaborated. • Fractional derivative Burgers model considering creep damage was created. • Effects of particle morphology on damage factor were investigated. • Effects of particle morphology on relaxation time were studied. [ABSTRACT FROM AUTHOR]

Conventional asphalt mixtures are prone to material separation during mixing, causing rutting, loosening, and cracking of the pavement. This study proposes a two-stage mixing method to address these problems. The theoretical asphalt film thickness is calculated, and a physical index of the mixture for aggregates with different specific surface areas is used to determine the optimal asphalt mixing ratio. The microstructural characteristics and mechanical properties of the mixtures are evaluated using digital image processing, laboratory experiments, and correlation analyses. The results show that the two-stage mixing process mixes the aggregate more thoroughly, resulting in a uniform asphalt film with sufficient contact between the asphalt particles, improving the mixture's mechanical performance. The results of this study can be used to improve the design of asphalt mixtures and reduce the use of petroleum asphalt. • The optimum mixing ratio of asphalt mixture for different mixing methods was obtained. • The two-stage mixing process mixes the aggregate more thoroughly, resulting in a uniform asphalt film. • The two-stage mixing method improved the mechanical properties of the mixtures. • The multi-scale evaluation system for different mixing methods was established. [ABSTRACT FROM AUTHOR]

Concrete-rock composite structures represent a distinct category in engineering structures. The interplay between concrete and rock entities is of great significance in engineering research, especially concerning the stability of concrete-rock foundations. Recently, numerous compression tests have been conducted to investigate the interplay between concrete and rock, but the influence of interface inclination angle and concrete component percentage on the mechanical properties and failure mechanisms under biaxial compression loading conditions has not been addressed by previous studies. Therefore, the main objective of this study is to systematically investigate the mechanical response of CGCSs under compression loads and different combination modes (concrete component percentage and interface inclination angle). In this study, high-precision digital images were used to characterize the spatial distribution of mineral and material components in granite and concrete specimens, respectively. By employing digital image processing (DIP) technologies along with the numerical simulation code (RFPA2D), numerical models capable of reflecting the meso-structures inside concrete-granite composite specimens (CGCSs) were reconstructed for the first time. The results indicate that these numerical models realistically reproduce the mechanical properties and failure characteristics of CGCSs, and four kinds of failure modes have been observed for different combination modes of CGCSs under compression load. There was a critical concrete component percentage, below which the peak strength of CGCSs surpassed that of the concrete specimen and above which the opposite trend was observed. Moreover, the peak strength of CGCSs was lower than that of the concrete specimen when the interface inclination angle ranged from 30° to 60°, with the opposite trend observed for other angles. Due to the lateral constraints, the peak strength and elastic modulus of CGCSs exhibit a positive correlation with confining pressure under different combination modes. The peak strength and elastic modulus of CGCSs exhibit a negative correlation with the concrete component percentage. As the interface inclination angle increases, the peak strength and elastic modulus of CGCSs initially decrease and then stabilize. These numerical results provide valuable insight into the mechanical properties and failure mechanisms of CGCSs under different combination modes and complex loading conditions. • Numerical models capable of reflecting the meso-structure inside concrete-granite composite specimens (CGCSs) were reconstructed by using digital image processing (DIP) technologies and RFPA2D code. • These numerical models realistically reproduce the mechanical properties and failure characteristics of CGCSs. • Four kinds of failure modes can be observed for different combination modes of CGCSs under compression loading conditions. • The confining pressure and combination mode have a significant effect on the peak strength and elastic modulus of CGCSs. [ABSTRACT FROM AUTHOR]

In consideration of skyrocketing price in pavement materials and ever-increasing attention to environment preservation, the utilization of reclaimed asphalt pavement (RAP) into asphalt mixtures has gained more and more favor. However, the uniformity of virgin and RAP aggregate in recycled asphalt mixtures (RAM) has not yet been studied due to the difficulty in informational extraction of multi-structures. To address this, white dolomite was used as virgin aggregate to be distinguished from RAP aggregate based on their color difference. The shear gyratory compactor specimens of RAM were prepared and saw-cut, and then meso-structures including virgin aggregate, RAP aggregate and asphalt mortar were extracted using digital image processing (DIP) technology. The DIP processes mainly involved retention of blue channel for image graying, newly proposed double-threshold OTSU method for image segmentation and image morphology processing. The aggregate uniformity index (AUI) was proposed based on the coefficient of variation of aggregate area ratio. The influences of RAP that were pre-processed by refined decomposition (RD) and routine crushing (RC) and its content on the aggregate uniformity were evaluated. The results demonstrate the efficacy of the improved DIP approach, as evidenced by the maximum mean absolute error between laboratory-measured and image-estimated gradation that is lower than 7.8 %. The addition of RAP into asphalt mixtures is adverse to the aggregate uniformity. The higher the RAP content, the inferior the aggregate uniformity of RAM, which attributes to the agglomeration of RAP materials. RAP agglomeration leads to the heterogeneity of RAP aggregate and thus dominates the heterogeneity of the whole aggregate. Therefore, the aggregate uniformity of RAM produced by RD-RAP is superior to that produced by RC-RAP. The superior degree is 9 %. • Dolomite is used as virgin aggregate to distinguish from RAP aggregate. • Double-threshold OTSU method for image segmentation in the recycled asphalt mixtures is proposed. • RAP has adverse effect on the aggregate uniformity due to its agglomeration. • Recycled asphalt mixtures with refined decomposed RAP show superior aggregate uniformity than routine crushed RAP. [ABSTRACT FROM AUTHOR]

Aggregate particles within an asphalt mixture are coated in a thin film of binder. Several studies and even mix design procedures have been based on an implied correlation between the binder film thickness (BFT) and the mechanical properties of these mixtures. Those studies generally define the BFT based on a predictive equation linked to one or more theoretical considerations, but without direct experimental determination of the BFT. However, recent developments of powerful scanning electron microscopes (SEMs) and digital image processing (DIP) techniques allow one to perform detailed analysis of microstructural images and thus directly measure the BFT value. This paper presents an analysis of BFT and mastic film thickness (MFT) within asphalt concrete (AC) mixtures using high-resolution SEM images and DIP. Based on this methodology, the measured mean BFT and MFT values ranged from 5.6 µm to 10.8 µm, and from 20.8 µm to 28.2 µm, respectively. Additionally, a BFT analysis considering fine aggregate matrix (FAM) phase within the ACs was pursued. A criterion to obtain FAM BFT is proposed and it can be useful in volumetric FAM mix design and sample fabrication. • A procedure to characterize BFT using SEM images and deep learning segmentation techniques is presented. • A representative scanning area of 16 mm2 was identified as appropriate to perform BFT characterization. • BFT followed lognormal distributions and its mean values ranged between 5.6 μm and 10.8 μm. • MFT followed gamma distributions and its mean values ranged between 20.8 μm and 28.2 μm. • Fine aggregate matrix BFT can be estimated using asphalt concrete standardized sieves. [ABSTRACT FROM AUTHOR]

This paper aims to study the effects of spatial corrosion distribution and prestressing levels on the structural performance of corroded prestressed concrete (PC) beams via experimental studies. X-ray and digital image processing techniques are developed to visualize and quantify the spatial corrosion of strands in PC beams. The spatial characteristics of strand corrosion and its effect on the structural performance of PC beams are compared with those of reinforced concrete (RC) beams. Unlike RC beams showing more ductile behavior, the load-bearing capacity and ductility of the corroded PC beams are determined by the maximum local steel weight loss of an individual steel wire in the maximum bending moment region. The prestress force induces larger local maxima of steel corrosion and widens corrosion-induced cracks by elevating the concrete tensile strain in the transverse direction. • The effects of spatial corrosion and prestressing forces on the structural performance of PC beams are investigated. • Spatial corrosion of a strand and wires is quantified using X-ray and digital image processing techniques. • Higher prestressed force induces larger corrosion cracks and maximum steel weight loss. • Ductility and loading capacity of PC beams are more sensitive to corrosion than RC beams. • Spatial steel corrosion must be considered to evaluate the performance of corroded PC beams. [ABSTRACT FROM AUTHOR]

Particle size distribution (PSD) is an important index property of granular materials. The conventional mechanical sieve analysis for PSD determination is labor-intensive, time-consuming, and inefficient. With the development of computer vision, image-based gradation prediction methods gradually emerge. However, previous studies mainly focused on images where individual aggregate particles are separated from each other without overlapping (i.e., not densely packed together), thus limiting their applicability. This study proposed a novel framework to predict the PSD of densely packed coarse aggregate particles, which is based on visual foundation model and machine learning with appropriate feature engineering. First, the segment anything model, which is one of the visual foundation models, was used for the instance segmentation of the densely packed aggregate particles. Then, the feature engineering was implemented with the segmentation result. The major quantitative indicators related to particle sizes were extracted from each mask, such as the area, the diameter of minimum enclosing circle, and the major axes of the equivalent ellipse of particle contours. Subsequently, the cumulative distribution curves of these indicators were plotted, followed by the data sampling operation on these curves to generate the feature vectors. The artificial neural network (ANN) model was then trained for gradation prediction with each of the feature vectors and the data points extracted from PSD curves used as the inputs and data labels, respectively. The model-training dataset consisted of 8160 different sets of digitally generated particle samples with known PSDs. Finally, verification test results of additional five different groups of digital particle samples and real coarse aggregate samples with known PSDs show that the root mean squared error (RMSE) of predicted aggregate sizes is 1.79 mm and 1.86 mm for digital and real aggregate particle samples, respectively. Therefore, the efficacy and feasibility of the proposed method was validated in terms of predicting the PSD of densely packed coarse aggregates from two-dimensional image information. As compared to the Mask RCNN combined with empirical formula of which the RMSE values were 5.12 mm and 5.84 mm for digital and real aggregate particle samples, respectively, the proposed framework demonstrates improved particle segmentation applicability and better PSD prediction accuracy. ● Densely packed particle gradation prediction based on image processing. ● Exploring segmentation foundation model for coarse aggregate particle image segmentation. ● Feature engineering for quantifying particle image contours. ● Digital particle dataset for gradation-predicting machine learning models. [ABSTRACT FROM AUTHOR]

Pervious concrete is a three-phase structure, including the pore phase, aggregate phase and paste phase. Currently, the pore phase of pervious concrete can easily be identified with CT and image processing technology. However, there are still technical limitations to identifying the aggregate and paste phases. This paper aims to establish a three-phase structure analysis method for pervious concrete based on deep learning. 30 images of the cross section of nine groups of color-pervious concrete specimens with different coarse aggregate sizes were obtained to form the initial dataset. The initial dataset was preprocessed, labeled and extended, and four deep learning frameworks were selected to train the three-phase structure identification models. By comparing and analyzing the identification effects of different models, the model suitable for three-phase structure identification was determined. The segmentation accuracy of each phase was evaluated. The results show that the method for preparing the dataset required for three-phase structure identification model training could improve identification accuracy. The model can accurately identify the three-phase structure with fewer false and missed identifications. The descending order of three-phase structure identification accuracy was pore phase＞aggregate phase＞paste phase. This was related to the quality of the dataset and annotation accuracy. • A method for preparing the data set required for three-phase structure identification model training was proposed. • Models for identifying the three-phase structure of pervious concrete were trained. • The Mask R-CNN model was evaluated and compared with other models. [ABSTRACT FROM AUTHOR]

To quickly obtain texture information and accurately evaluate the skid resistance of asphalt pavement, a multiview stereo reconstruction method based on deep learning is proposed for evaluating the texture depth of asphalt pavement. First, a depth camera and a digital camera are used to collect RGB-D (i.e., Red, Green, Blue, Depth) and RGB (i.e., Red, Green, Blue) multiview image datasets on different types of pavements for model training and validation respectively. Then, the model precision is validated by calculating the overlap (intersection over union, IoU) between the ground truth point cloud and the reconstructed point cloud. The model performances under different training strategies are compared to obtain the best model, and the effects of image resolution, number of views, and types of pavement material on the model precision are analyzed. Finally, image processing methods and texture depth characterization indexes are proposed to obtain pavement texture information from the reconstructed depth map, thus validating the effectiveness of the model. The results show that the trained model using the transfer learning strategy achieves a reconstruction precision of 0.77 when the image resolution is 3024 × 3024 and the number of views is 7. Furthermore, the reconstruction performance remains stable across different pavement materials, suggesting the model is suitable for accurately reconstructing depth maps for asphalt pavements. The errors between the predicted texture depth value calculated based on the volume-based index (MTD p) and the profile-based index (MPD p) using the depth maps after image processing and the measured texture depth value (MTD e) using the sand patch method are 11.72% and 18.85%, respectively. It is believed that the pavement texture depth can be effectively evaluated from the reconstructed depth map using the volume-based metric (MTD p). In contrast to traditional testing methods, this approach requires using only a digital camera and a personal computer, making it a lightweight and intelligent analysis method for obtaining pavement texture depth information. ● A method for MTD prediction based on reconstructing depth maps is proposed. ● Establishment of an RGB-D dataset and an RGB dataset of pavement texture images. ● Different training strategies are utilized to improve model performance. ● Analysis of various factors affecting model precision is conducted. ● Evaluation metrics in different dimensions are developed. [ABSTRACT FROM AUTHOR]

The fineness modulus(FM) represents the level of particle size of manufactured sand. Real-time feedback of FM of manufactured sand is important for industrial sand production, but extracting the particle profile from densely stacked images is a great challenge. In this study, a deep learning and regression analysis-based online measurement method for FM of manufactured sand is proposed. Firstly, the real fineness modulus of the sand produced by the sand-making machine in real time was obtained by the vibration-screening method(VSM). Then, the particle size fraction of larger particles (0.6–4.75 mm) was obtained based on machine vision combined with a convolutional neural network and image processing. Secondly, a multiple linear regression model was developed for the percentage of particle size and FM. Finally, the percentage of particle size was substituted into the regression model as the independent variable to achieve a fast prediction of the unknown FM. The experimental results show that the maximum repeatability errors for FM of different manufactured sands are 0.09 and 0.13 respectively, and the maximum absolute errors of the FM prediction results are 0.18 and 0.17 respectively. The calculation efficiency and error level of this research method can meet the online testing at sand making sites. • The PSD of the manufactured sand is close to normal distribution. • Image acquisition of stockpiled manufactured sand by means of machine vision. • Combining deep learning and image processing for segmentation of manufactured sand images. • a multiple linear regression model was developed for the percentage of particle size and FM. • A fast and accurate online measurement method for fineness modulus of manufactured sand is proposed. [ABSTRACT FROM AUTHOR]

The mechanical properties of the asphalt mixture largely depend on its internal structure. Studies in mixture mesostructure are of great significance for perfecting relevant theoretical systems and improving mixture design methods. However, efficient segmentation of mesostructure is an important precondition for mesoscale research about asphalt mixtures. Therefore, this article proposes a new method for mesostructure information acquisition that mainly improves the function of image preprocessing and separation of touching objects. A visual and quantitative comparison between the proposed and common methods is adopted. The result shows that the proposed method performs better in identifying small particles and retaining the fidelity of the boundary. Besides, the mesostructure of four types of asphalt mixtures are compared based on the proposed method. The result shows that the optimum sample size, which makes the measured 2D information efficiently close to the actual 3D information, is around 20–30. Compared to SUP25, the coordination number of AC20 is about 0.15–0.23 higher. Compared with the samples compacted by Marshall, the samples compacted by gyratory have a larger coordination number, about 0.12–0.21 higher, a larger fractal dimension, about 0.059–0.062 higher, a smaller uniformity of major axis orientation, about 1.53–1.28 lower, and a smaller uniformity of area distribution, about 0.063–0.12 lower. • Proposes and verifies a new method for mesostructure information acquisition. • Multiple samples can remedy the information loss caused by dimension reduction. • The optimum sample size is determined to be around 20–30. [ABSTRACT FROM AUTHOR]

The damage to microbial solidified engineering residue by freeze-thaw cycles increases the amount of material prone to wind erosion. Microbial solidification of engineering residue was carried out, and freeze-thaw cycle and indoor wind tunnel tests were conducted on the microbial solidified samples to reveal the interaction mechanism between different numbers of freeze-thaw cycles and the wind erosion degree. The test results showed that the larger the number of freeze-thaw cycles, the greater the mass loss of the microbial solidified engineering residue sample due to wind erosion and the lower the surface strength and surface thickness of the sample. However, the surface strength and surface thickness were relatively stable after more than 7 freeze-thaw cycles. The mass loss of the sample was 13 g after 9 freeze-thaw cycles at the maximum wind speed (15 m/s), higher than that of the sample exposed to no freeze-thaw cycles (6 g) but far lower than that of the undisturbed sample (3647 g). The results indicated that the microbial solidified engineering residue had high freeze-thaw resistance. The microbial solidified engineering residue was analyzed by computed tomography (CT) before and after the freeze-thaw cycles, and three-dimensional reconstruction was performed using digital image processing. The microstructure analysis showed that the freeze-thaw cycles did not change the content and spatial distribution of the microbial solidified products but reduced the ability to cement the microbial solidified products and the soil particles. The calcium carbonate inside the hard shell became more fragmented, the equivalent radius of the crystals and the stability of the hard shell decreased, and the porosity increased. However, the microbial solidified engineering residue exhibited high resistance to wind erosion and freeze-thaw cycles. • The microbial solidified sample became relatively stable after 9 freeze-thaw cycles. • A three-dimensional reconstruction of the microbial solidified sample. • The equivalent radius of the pores decreased. • The calcium carbonate inside the hard shell became more fragmented. • Microbial solidified sample exhibited high resistance to erosion. [ABSTRACT FROM AUTHOR]

Void is an important manifestation of the strength of asphalt mixture, but few studies have focused on the change of voids during the compaction of asphalt mixtures. The voids of SMA-13 and OGFC-13 were selected as the research objects, and the compaction process of the asphalt mixture was analyzed based on different compaction degree specimens in lab through gyratory compactor. Firstly, CT scanning was used to extract the void structure of the asphalt mixture at different degrees. Secondly, based on the 26-connectivity algorithm, the connected voids and closed voids of asphalt mixture were extracted, and the volume and particle size distribution were analyzed. Thirdly, the gradation of voids was analyzed by the two-parameter Weibull function, and the morphological characteristics of voids were analyzed by roundness, sphericity, and abundance. The results showed that the distribution of cross-section voids ratio and the number of voids of the SMA-13 was more uniform than that of OGFC-13. The characteristic parameters of two-parameter Weibull function showed that with the increase of specimen density, the three-dimensional void size became smaller and smaller, and the void size distribution became more and more uniform. The sphericity and roundness of the voids of SMA-13 and OGFC-13 were less affected by the compaction degree, and the void abundance of the OGFC-13 gradation asphalt mixture was greatly affected. For the connectivity of voids, the closed void volume of OGFC-13 increased with the increase of compaction degree, but the maximum proportion of closed voids in the total void volume does not exceed 10% during the compaction process. For the gradation SMA-13, the compaction degree of 90%∼93% was the boundary interval of connected voids from existence to nonexistence, which may be a critical interval for forming skeleton strength during the compaction process. • The asphalt mixture compaction process was decomposed based on gyratory compactor. • The CT scanning method in asphalt mixture compaction was proposed. • The meso-structure of voids in asphalt mixture during compaction was characterized. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (X-ray CT) and spherical harmonics (SH) reconstruction technology were combined to quantify the content, distribution and wrapping degree of residual mortar in the recycled concrete aggregate (RCA). The RCA was submerged in 30 wt% CsCl solution, and scanned by using X-ray CT to determine its surface profile coordinates. The RCA' morphology and its original aggregate were reconstructed by using SH reconstruction technology to determine the content, distribution and wrapping degree of residual mortar. The optimal reconstruction parameters were analyzed and the proposed method was compared with the methanol salicylate solution test. The results indicate that the optimal number of reconstructed sample points by SH function was 260 points at each angle. The error between the residual mortar's content obtained by the proposed method and the methanol salicylate solution test was <5 %. The residual mortar's content was not always consistent with its distribution and wrapping degree. • Residual mortar's distribution and wrapping degree were quantified. • Residual mortar's wrapping degree was not necessarily proportional to its content. • Contact area between residual mortar and original aggregate could be reflected. [ABSTRACT FROM AUTHOR]

At a micro-scale level, grouted porous asphalt mixtures are multiphase composites consisting of aggregates, asphalt mortar, filler, asphalt mortar-aggregate interfacial transition zone, and asphalt mortar-filler interfacial transition zone. It exhibits different mechanical behavior under different temperatures and loading conditions. Compared with aggregate, asphalt mortar, and filler, the interfacial transition zone (ITZ) is more prone to damage at low temperatures, with both the fine structure and the nature of the contact interface having a significant effect on the overall strength and crack resistance of the material. To evaluate the influence of ITZ and filler material properties on the cracking resistance of the material, a grouted porous asphalt mixture model based on the real aggregate shape was established using finite element modeling. A cohesive zone model (CZM) was employed to characterize the internal mechanical behavior of the material, and a mesoscale damage simulation was performed for the grouted asphalt mixture. The study revealed the influence of different interfacial and filler material properties on the low temperature cracking resistance and proposed a design approach for anti-cracking grouted asphalt mixtures. The findings indicated that interfacial transition zone (ITZ) and filler properties play a crucial role on determining the damage characteristics. Increasing the ITZ and filler properties significantly enhanced the low temperature crack resistance of the grouted asphalt mixture. • Combine machine learning image processing to establish a microscopic model for permeable porous asphalt mixture. • By experimental results, it is concluded that improving the strength of fly ash and ITZ is beneficial to enhancing the crack resistance of pervious asphalt mixture. • By experimental results, it is concluded that adding coupling agents and polyester fibers can enhance the interfacial flexural strength of ITZ. [ABSTRACT FROM AUTHOR]

Plastics have proven as effective modifiers for asphalt binders; however, significant knowledge gaps exist around appropriateness of standard method of storage stability test and phase separation mechanism. These gaps are even more critical when using waste plastics containing polymeric cross-contamination. This study addresses these gaps through the application of asphalt tests, fluorescence microscopy (FM) and image processing techniques. The FM and image processing results showed that there is a relationship between elevated plastic swelling rates and plastic dispersion in bitumen matrix. Quantitative analysis of the images indicated that LDPE and LLDPE modified binders exhibited the lowest level of phase separation (5 %), followed by HDPE modified binder (13 %), and PP modified binder showed the highest level (20 %). Furthermore, cross-contamination of LDPE, LLDPE, and HDPE with PP notably worsened the storage stability of the resulting modified binders by approximately 10 %. The findings from FM and image processing indicated that the softening point test conducted through the storage stability tube test is insufficient for assessing the storage stability of plastic modified binders. To further validate this point, asphalt performance was examined for asphalt mixtures made with the binders before and after storage stability test. The findings demonstrated a substantial influence of phase separation of binder on key properties of the resulting asphalt mixture. • This research provides new insights into the swelling rate and dispersion of plastics in bitumen. • Cross-contamination of plastics with polypropylene exacerbates the compatibility with bitumen. • LDPE and LLDPE cross-contamination have limited effects on the compatibility with bitumen. • The common storage stability test is ineffective for measuring phase separation for some plastic-modified bitumen. • This study introduces a novel method for measuring phase separation in plastic-modified bitumen using image processing. [ABSTRACT FROM AUTHOR]

Porous asphalt concrete (PAC) has attracted significant attention in the road industry due to its excellent noise reduction capabilities. However, clogging in PAC decreases its sound absorption performance, as mesoscopic pore parameters play a crucial role in influencing PAC's sound absorption abilities. To further investigate the influence of mesoscopic pore parameters on the sound absorption performance of PAC, CT scanning and digital image processing technology were employed. The principal component analysis method was employed to determine the weighting of the influence of mesoscopic pore parameters on the sound absorption coefficient. The correlation between different pore parameters and the sound absorption coefficient of PAC specimens was analyzed. The results showed that the minimum Feret diameter has the most significant effect on the sound absorption performance of PAC, while the concavity and the length-short axis ratio have the least impact. The correlation between mesoscopic pore parameters and sound absorption coefficients was found to be inconsistent before and after clogging. Mesoscopic pore parameters that were positively correlated with sound absorption coefficients before clogging turned negatively correlated after clogging. Connected large pores and smooth regular pore structure enhance the sound absorption performance of PAC, but clogging disrupts this structure, resulting in a decrease in sound absorption performance. • The mesoscopic pore parameters were screened by principal component analysis. • The minimum Feret diameter has the greatest effect on the sound absorption performance of PAC. • Clogging disrupts PAC's smooth, interconnected pores, causing decline in its sound absorption performance. [ABSTRACT FROM AUTHOR]

For fiber reinforced permeable concrete, numerical simulation method have been applied to reduce the time consumption of physical experiments and to study the mesostructure of the material. In this paper, polyacrylonitrile fiber reinforced permeable concrete (PANFRPC) is considered to consist of aggregate, mortar (containing fibers inside), interface transition zone (ITZ) and pores in 3D mesostructure model generated from computer tomography (CT) images. For the numerical simulation of the, material properties of each component in the model are assigned according to physical experimental results. The failure curve of the mortar in PANFRPC is determined by physical test results to reflect the effect of fibers. The simulation results show that the model has similar mechanical properties to the actual specimen and can be used to simulate the elastic stage of the uniaxial compression process and predict the strength with an error of less than 15% despite the simplification of fiber. the accuracy and reliability of the model have also been verified by the distribution pattern of vertical caraks and x-shaped shear cracks from macro and meso perspectives. • CT scanning and digital image processing technology are applied to extract the mesoscopic components of PANFRPC specimen. • 3D mesostructure model of PANFRPC is established for numerical analysis of uniaxial compression process. • The accuracy and reliability of the numerical model construction method are verified by simulation results. [ABSTRACT FROM AUTHOR]

Over the last two decades, the integration of big data and deep learning technologies has demonstrated remarkable effectiveness across various domains of civil engineering, leading to significant advancements in machine learning methodologies. Regardless of significant progress in various aspects of civil engineering, there appears to be a striking absence of thorough and all-encompassing evaluations that adequately cover critical domains such as structural damage identification (SDI), accurate prediction of concrete properties, precise crack detection, detailed analysis of crack properties and thorough non-destructive testing (NDT) methods applied specifically to concrete structures. This gap in the existing literatures hinders a comprehensive understanding and application of advanced techniques in these crucial areas. To bridge this knowledge gap, this paper offers an all-encompassing exploration of the recent developments and utilizations of artificial intelligence in civil engineering. Emphasizing concrete-centric structures, this study prioritizes two crucial facets: the automatize detection of concrete specific mechanical attributes and crack detection. To achieve this numerous recent surveys have utilized intelligent algorithms primarily grounded in image statistics, coupled with extensive data analytics and deep learning methodologies. By delving into this research, the paper sheds light on the trajectory and challenges encountered by artificial intelligence in civil engineering over the past few years. It highlights studies that emphasize structural maintenance and management, quality control, design optimization, and more, accomplished through image processing and computer vision techniques. In essence, this review seeks to unveil the profound impact of artificial intelligence in transforming civil engineering practices, with a strong emphasis on exploiting image processing and computer vision methods to tackle concrete-related issues. • Impact of image centric artificial intelligence to predict concrete properties. • Features Non-Destructive Testing methods, digital image-based setups on concrete. • Offers qualitative analysis of research trends in computer vision and data mining. • Intelligent algorithms aid for concrete structural maintenance and quality control. • Challenges and advancements of concrete technology based on artificial intelligence. [ABSTRACT FROM AUTHOR]

Mastering a lightweight method for the identification of pavement surface cracks plays a vital role in enhancing the efficiency and accuracy of pavement crack detection. However, the existing crack detection methods typically concentrate on improving model performance while neglecting the study of lightweight identification methods, causing inefficient crack detection. Therefore, this study aims to investigate a lightweight identification method for pavement cracks. High-definition vehicle-mounted cameras were employed to collect pavement crack images across multiple districts in Shenzhen, China, resulting in a dataset containing 20256 images. In addition, a pavement crack detection method based on the You Only Look Once version 8 (YOLO v8) model was proposed. On this basis, a knowledge distillation model enhanced by multiple teacher-assistants (KDMTA) was established to explore the impact of different teacher assistants and distillation paths on model performance. The results indicate that in the proposed KDMTA model, each level of teacher-assistant (TA) model can absorb diverse aspects of knowledge from the teacher models, thus broadening the range of insights. Subsequently, the information is imparted to lower-level TA models and the student model, expanding the perspective from which the student model acquires knowledge. Therefore, this improves the model's generalization performance and its effectiveness in target detection. The type and number of teacher assistant models, as well as the distillation path, influence the model performance. The random learning strategy enriches the combination and transfer of knowledge among teacher models, TA models, and student models, achieving lightweight identification for pavement crack. According to the results, the crack identification accuracy reaches 95.79 %, the mAP reaches 81.07 %, and the image processing speed has been improved by 79.6 %. This study enhances the efficiency and accuracy of pavement crack detection, providing methodological support for the rapid and accurate identification of pavement cracks. This study offers a novel method for the rapid and accurate identification of pavement cracks, which can assist management authorities to timely identify and repair pavement defects, reducing maintenance costs, and extending the lifespan of roads. Moreover, it can also facilitate the development of road safety management and intelligent transportation systems. [Display omitted] • Establish a large-scale and high-quality dataset suitable for pavement crack recognition. • Clarify the performance evolution pattern of YOLOv8 model. • Determine the impact of the teacher-assistant models on knowledge distillation. • Develop knowledge distillation model enhanced by multiple teacher-assistant and random learning. [ABSTRACT FROM AUTHOR]

Limestone Calcined Clay Cement (LC3) plays a pivotal role in the transition of cement industry toward lower carbon emissions. However, the reliance on limited, non-renewable limestone and expensive, high-grade kaolinite presents notable challenges for LC3, requiring a balance between using lower-quality materials and maintaining high performance. To address this challenge, this study introduced an innovative LC3 mix by incorporating shell powder and self-ignition coal gangue as substitutes for traditional ingredients. The study found that shell powder significantly increased the early-age compressive strength of LC3 at 3 days, raising it to 20.4 MPa, although it restricted the space for further hydration product development. In contrast, introducing self-ignition coal gangue fine-tuned the hydration process, resulting in enhanced compressive strengths of 34.3 MPa and 45.2 MPa at 7 and 28 days, respectively. Additionally, Backscattered Electron (BSE) analysis revealed notable microstructural heterogeneity and introduced a new image processing-based method for evaluating microstructure, emphasizing the importance of adjusting phase compositions around inert particles and reducing inter-particle overlap to optimize LC3 performance. • Novel LC3 formulation leverages solid wastes for improved strength and sustainability. • Shell powder and self-ignition coal gangue identified as effective substitutes in LC3 matrix. • Potential for self-ignition coal gangue in optimizing LC3 carbon footprint. • New directions for enhancing LC3 using controllable particle metrics suggested. [ABSTRACT FROM AUTHOR]

Employing 3D-printed permanent formwork (3DPF) technology for rapidly manufacturing prefabricated components constructed with reinforced concrete represents a promising application of additive manufacturing technology in load-bearing constructions. Ensuring efficient bonding between 3DPF and post-casted concrete (PCC) is a prerequisite for the widespread adoption of this technology. This research aims to enhance the 3DPF-PCC interface through: physical (groove), chemical (cement paste) and composite methods. The interfacial bond strength was quantitatively analysed by splitting tensile strength and shear strength. X-ray computed tomography scanning was utilized for the quantitative characterization of pore structures. Digital image processing techniques were employed to monitor crack development during splitting tensile tests. The mechanism for strengthening the shear strength of the grooved interface was elucidated using a finite element model, and a modified formula for calculating the shear strength of the 3DPF-PCC grooved interface was proposed. The physical method improves shear strength by increasing interlocking structures significantly. The composite method weakens mechanical interlocking structures, increases interface porosity, and adversely affects interface bonding strength. It is recommended that future engineering applications adopt the physical process to improve mechanical interlocking structures and ensure interface bonding strength. • Three methods were used to enhance the interface between 3DPF and PCC. • The shear strength can be enhanced more than 25 % by increasing interlocking structures. • Composite method causes a significant fluctuation in the interface strength. • A modified formula was used to calculate the shear strength of the interface. [ABSTRACT FROM AUTHOR]

The computer vision-based analysis of railway superstructure has gained significant attention in railway engineering. This approach utilises advanced image processing and machine learning techniques to extract valuable information from visual data captured in the railway track environment. By analysing images from various sources such as cameras, drones, or sensors, computer vision algorithms can accurately detect and classify different components of the ballast superstructure, including the catenary system support, rail surface and profile, fastening system, sleeper, and ballast layer. This enables the automated assessment of the railway track's condition, stability, and maintenance needs. This paper comprehensively reviews the recent advancements, challenges, and potential applications of computer vision techniques in analysing railway superstructure. It discusses various vision-based methodologies and machine-learning approaches utilised in this context. Furthermore, it examines the benefits and limitations of computer vision-based analysis and presents future research directions for improving its applicability in railway track engineering. • Explores advanced sensing technologies and robotics. • Discusses image preprocessing, segmentation, and detection methods. • Examines track components: ballast, sleeper, fasteners, rail, and catenary system. [ABSTRACT FROM AUTHOR]

This paper focuses on the tunnel cracking disease under biased pressure. Through model tests on three lining conditions, the study investigates the expansion process of surface cracks on the lining under biased pressure. Proposing an improved method for calculating fractal characteristics of lining crack disease based on box counting dimension. Quantifying crack disease through digital image processing to obtain two parameters: total width and fractal dimension. Discussed the relationship between quantification parameters of surface crack disease in linings and the service performance of the lining. 29 groups of crack disease quantization parameters obtained by model test were selected to establish the disease sample space, and the spatial data were automatically learned and grouped based on clustering method. Finally, a new diagnostic Index of Tunnel Lining Defect Index-Crack & Dimension (TLDI-C&D) and its disease classification criteria for the whole lining ring are proposed. The results indicate that under the biased pressure, regardless of the presence of pre-existing defects, the biased pressure region is the most severely diseased area in terms of cracking. Compared to the intact lining, the initial cracking loads for arch crown defects and arch waist defects are advanced by 33.1 % and 73.0 %, respectively. After the apparent cracks in the lining are fully formed, pre-existing defects no longer affect the speed and distribution of crack propagation.The larger the fractal dimension, the more complex the morphology of the corresponding crack disease. Under the same image area, the ratio of crack quantity to width has a greater impact on the fractal dimension. There is a corresponding relationship between quantification parameters of crack disease and the bearing performance of the lining structure, making them indicators for measuring the load-bearing capacity of the lining. The most reasonable grading level for lining crack disease under the biased pressure is four levels. The diagnostic index for tunnel lining cracks, TLDI-C&D is 1.0～2.2 (level I), 2.2～3.2 (level II), 3.2～4.3 (level III), and 4.3～5.5 (level IV). On-site measurements have proven the accuracy of this crack disease diagnostic evaluation method. • The fracture characteristics of lining with different existing defects under biaed pressure were analyzed. • An improved method for calculating the fractal dimension of the whole ring lining is presented. • The fracture disease evaluation index is established based on the crack width and fractal dimension. [ABSTRACT FROM AUTHOR]

To effective recycle the Municipal solid waste incineration bottom ash (MSWIBA), this study adopts cold consolidation granulation technology to develop eco-friendly artificial lightweight aggregates (ALCBAs), and the materials are over 50 % dosage of MSWIBA, less 50 % fly ash (FA) or ground granulated blast furnace slag (GGBFS), and 10 % ordinary Portland cement (OPC). This study investigated the effects of different binder types and dosages on the properties of ALCBAs, including compressive strength, water absorption rate, bulk density, microstructures, pore development, heavy metal leaching behavior, and sustainability. The feasibility of ALCBAs to design green concrete was also explored. The results show that with higher content of MSWIBA and binders of FA, GGBFS and OPC, the ALCBAs with compressive strength greater than 2.5 MPa, bulk density of about 1000 kg/m3 and water absorption rate greater than 12 % was successfully developed. Increasing the content of FA/GGBFS is beneficial to improve the compressive strength and reduce the water absorption rate and the total porosity and lower the air bubbles percentage of ALCBAs. More importantly, both FA and GGBFS are mixed into ALCBAs, the compressive strength is at 2.5 MPa-3.0 MPa, the water absorption rate is significantly reduced, and the energy consumption, carbon footprint and cost are also significantly reduced. As the content of ALCBAs increases, although the compressive strength and splitting tensile strength are not as good as concrete with natural aggregates, the density of low-carbon concrete with ALCBAs gradually decreases, and the interface transition zone is gradually improved. Overall, ALCBAs can be beneficial to the conservation of natural resources (natural aggregates) and effectively solve the shortage of natural aggregates, and can be used to deal with the accumulation of solid waste in a green way, and it also can open up new channel for solid waste recycling. • The ultra-high volume of MSWIBA powders is recycled to design low-carbon ALCBAs. • The compactness of ALCBAs can be characterized by the image processing mrthods. • Incorporating FA and GGBFS to replace 20 % MSWIBA can raise the strength of ALCBAs. • The granulation efficiency of the ALCBAs can be improved with the rising content of FA or GGBFS. • Converting the MSWIBA powders into ALCBAs can decrease the leaching toxicity obviously. [ABSTRACT FROM AUTHOR]

As a solid waste from steel making, steel slag can be recycled to replace natural aggregates (NA) for developing steel slag aggregate (SSA) concrete, an environmentally friendly construction material. In order to evaluate the effect of steel slag on the permeability of SSA concrete, which may have a potential impact on the structure durability, this study presented a multi-scale finite element (FE) method for investigating the seepage process and mechanism inside the SSA concrete. The above FE method consisted of a microscale mortar model and a mesoscale concrete model, developed by integrating image processing technique and random aggregate method. The permeability coefficient of SSA concrete was evaluated with the above multi-scale FE method and was validated with experiment. The relative differences between the numerical simulation and experimental mean values of the permeability coefficients are 3.38∼5.94 %. Based on the validated method, the effects of steel slag on the seepage characteristics of concrete were analyzed. Results show that the incorporation of steel slag could decrease the permeability of concrete by reducing the mortar porosity in the microscale and reducing the thickness of the interfacial transition zone (ITZ) between aggregate and mortar in the mesoscale. Using over-simplified aggregate shapes in numerical simulation may underestimate the permeability coefficient of concrete, which emphasizes the importance of refined modelling. The permeability coefficient of concrete may exhibit anisotropy. When considering lateral water permeation, aggregates with aspect ratios close to one may offer superior resistance to seepage. The analysis findings provide insights for better material design of concrete with recycled materials. • Developed and validated multi-scale model of concrete containing steel slag aggregate for prediction of permeability. • Measured permeability of concrete and aggregate and observed interfacial transition zone of concrete using microscopy imaging. • Analyzed effects of steel slag on seepage characteristics and permeability of concrete. [ABSTRACT FROM AUTHOR]

Soil moisture loss, crack initiation, and propagation can severely damage the structural integrity, weakening the function and stability of soil, causing various problems in geological, geotechnical, hydraulic, environmental engineering fields. The aim of this study is to enhance the water-retaining property and cracking resistance of soil by adding sisal fiber and polyacrylamide. Laboratory tests were conducted to investigate the changes in moisture evaporation, crack initiation, and propagation process in modified clayey soil during three wetting-drying cycles. Soil mass was collected by weighing equipment and the evolution of surface cracks during the wetting-drying cycles were monitored. The resulting photographs of the cracks were further quantified and analyzed (using image processing technology). Results show that sisal fibers prevent the crack from developing downwards. In addition, the frictional force between soil particles and sisal fibers partially counteracts tensile stress when pulled horizontally, reducing the cracks' openings. On the other hand, polyacrylamide combined with water and soil particles to form agglomerates can reduce the evaporation rate, keeping the soil moist for a long time and delaying crack initiation. However, the soil's cracking resistance and water-retaining properties cannot be improved simultaneously when sisal fiber or polyacrylamide is present individually. Furthermore, adding only polyacrylamide increases the difficulty in the cracks healing via filling during the wetting-drying cycle. The composite of these two materials and reasonable control of the content can effectively solve the above problems. The optimal content of sisal fiber and polyacrylamide for enhancing water-retaining property and cracking resistance in clayey soil are 0.6% and 0.5%, respectively. The results of the study can provide some references for soil improvement in foundation treatment, soil and water conservation, and geological disaster prevention and other fields. • Fiber can amplify the anti-cracking effect during the rewetting and redrying. • Polyacrylamide extend the duration of evaporation. • Polyacrylamide delay the crack initiation. • Composite improvement enhances soil's water retention and anti-cracking. [ABSTRACT FROM AUTHOR]

This study aims to investigate the meso-structural evolution characteristics of porous asphalt mixtures (PAM) under stress and their effects on drainage and noise reduction environmental functions. Digital image processing technique was used to accurately identify the internal voids during uniaxial compression testing. The focus was on analyzing and simulating meso-scale differences in internal void structures before and after deformation and quantifying the evolution of these voids during the strain process. Drainage and noise reduction tests were conducted, and correlation analysis and factor screening were performed. The results indicate that in stress-induced meso-structural evolution, contact between aggregates generates force chains, and with increasing strain, the length of these chains and the number of aggregate contacts increase. Significant forces occur at the interface between aggregates and asphalt mortar. Aggregates undergo relative translational motion due to resistance to loads, resulting in compressed voids, cracks between asphalt mortar and aggregates, and gradual deterioration of the mixture structure. Additionally, the increase of strain significantly reduces the drainage and noise reduction performance of porous asphalt mixtures. The scale relationship diagram between macro- and meso-scale indicators identifies strain, void area and void sphericity as the primary influencing factors. The research findings contribute to understanding the stress transfer characteristics of PAM at the meso-structural scale, thereby enabling adjustments to grading, optimization of void structures, and enhancement of their long-term environmental functions. • Comparison of compaction methods and their effects on void distribution and stability. • Investigation of meso-structural evolution of porous asphalt mixtures under stress induction. • Analysis of the effect of meso-structural evolution on environmental functions such as drainage and noise reduction. • Importance of adjusting gradation and void parameters for achieving specific functionalities in porous asphalt pavement. [ABSTRACT FROM AUTHOR]

As materials undergo large-scale yielding or exhibit large sizes of fracture process zone in the crack tip region, multi-parameter fracture concepts should be employed to describe the complex crack-tip stress-strain fields. Fracture resistance curves (R-curves) are an established tool in characterizing the entire fracture process of such materials. However, for complex materials such as bituminous mixtures, the development of these curves is subject to experimental and computational intricacies. In this research, a framework is developed to automate the construction of R-curves for normal and rubberized asphalt concrete (AC) mixtures. AC mixtures are produced using PG58–22 and PG58–28 binders. Limestone and siliceous aggregates are used, and three binder contents are considered for the mixtures. Single-edge notched beam (SE(B)) fracture testing is conducted on AC beams with two different notch patterns. A convolutional neural network (CNN) model is developed and trained over 1260 test images with varying temperatures, notch geometries, and setups. The CNN model was used to detect the growing crack on the beam surface and each crack-detected image was sent to an image processing framework to measure the crack length. Crack extension increments were calculated and synchronized with test time and magnitude of load, load-line displacement, and cumulative fracture energy, and the R-curve could be constructed. A training accuracy of 0.91 was obtained for the model and a loss of below 0.10 as a result of a hyperparameter tuning indicating reliable classifications by the CNN architecture. The R-curves showed desirable agreement for control mixtures at temperatures of 0 °C and −15 °C. As the mixtures are rubberized, the R-curves showed favorable agreement in the crack blunting phase, transition zone, as well as the unstable propagation phase at −20 °C. Cohesive energy magnitudes were compared for the two methods with a Pearson coefficient of 0.81 while fracture rate and fracture energy magnitudes showed favorably close magnitudes with coefficients of 0.89 and 0.98 respectively. • R-curves of asphalt concrete mixtures were constructed using an automated procedure. • Image data pre-processing is conducted using CNN to classify images containing cracks. • The accuracy of the CNN model was enhanced to 0.91 after hyperparameter tuning. • The R-curves for rubberized mixtures were desirably close in all fracture phases. [ABSTRACT FROM AUTHOR]

Heterogeneous void distribution is a common phenomenon in asphalt mixtures and is closely linked to their performance. Therefore, it is necessary to propose a physically meaningful and accurately fitting vertical void distribution model for asphalt mixtures, so as to realize the quantitative evaluation of void distribution. In this study, virtual and actual specimens of asphalt mixture were established using discrete element method (DEM), laboratory compaction, and field coring methods. The vertical void distribution of different specimens was obtained through CT scanning and image processing technology. The impact of various factors, including gradation type, nominal maximum aggregate size, and specimen height on void distribution, was investigated. Subsequently, a three-stage exponential model was proposed to characterize the vertical void distribution in asphalt mixture. The physical significance of different fitting parameters in the model was elucidated. The results indicate that the vertical void distribution of DEM models and laboratory compaction specimens exhibited a bathtub shape, while the vertical void distribution of pavement core specimens exhibited a semi-bathtub shape. The void distribution of the mixture was affected by gradation type, nominal maximum aggregate size and specimen height. The vertical void distribution of asphalt mixture could be effectively characterized by the three-stage exponential model. In addition, the root mean squared error of the second stage data from the three-stage exponential model was also proposed to evaluate the void distribution uniformity. • A three-stage exponential model was proposed to characterize the vertical void distribution. • The feasibility of the three-stage model was analyzed through simulation and test results. • An evaluation method for the uniformity of vertical void distribution was proposed. • The potential application of the three-stage exponential model was discussed. [ABSTRACT FROM AUTHOR]

This research investigated the reinforcement mechanism of steel slag recycling in asphalt concrete from the perspective of adhesion and skeleton. Bonding performance between steel slag and bitumen was assessed by the calculation of coating area and peeling area, as well as the area detection by Scanning Electron Microscope (SEM) and Energy Dispersive Spectrometer (EDS). Computed Tomography (CT) scanning was used to determine the bitumen absorption characteristics of steel slag. Mechanical characteristics of steel slag mixture during compaction period and service period were evaluated by continuous compression test with Universal Testing Machine (UTM-25). A combination of CT scanning and image processing technology were used to quantify skeleton contact characteristics. The results indicated that bonding performance of bitumen with steel slag was obviously better than basalt after long-term erosion. The surface pores of steel slag absorbed excessive bitumen as anchoring and embedding effect, accounting for 20.24% of the total steel slag bitumen absorption in maximum. Steel slag skeleton of SMA gradation was found easily to be compacted during construction period and more resistant to loading during service period. In addition, there were more contact points in steel slag asphalt concrete, resulting that steel slag asphalt concrete had better stability. The contact points were different in different particle size ranges, and it was suggested that steel slag with particle size of 9.5–16.0 mm and 2.36–4.75 mm should be preferred. • Bonding of bitumen with steel slag was superior to that of basalt after long-term erosion. • Pores on surface of steel slag result in excessive bitumen adsorption, accounting for over 20% of steel slag bitumen adsorption in maximum. • Steel slag skeleton can better resist external loading to prevent early rutting diseases. • Steel slag asphalt concrete had better skeletal contact characteristics, while gradation type has greater influence than particle type. [ABSTRACT FROM AUTHOR]

This study presents a deep convolutional neural network metasensor for measuring crack width from high-resolution images and brightness profiles. Procedures of training, testing, data drift evaluation and fine-tuning were proposed. The metasensor enables repeated, semi-automated crack measurements in the same set of multiple locations over subsequent multi-stage observations, a unique and important feature for accurately assessing the progress of self-healing. It was employed to investigate the autogenous self-healing of 75-month-old high-strength concrete samples extracted from a structural member. The crack-width measurements, aided by X-ray computed tomography and electron microscopy data, helped to identify differences between the self-healing of externally exposed and deep-lying materials as a result of the natural local porosity differentiation. • Proposed deep CNN metasensor for crack width assessment from high-resolution images. • Trained metasensor using considerable set of manual reference measurements. • Achieved semi-automatic, multi-position and multi-stage self-healing evaluation. • Shown differences in healing of exposed and deeper materials, explained by porosity. • Demonstrated effective self-healing in mature high-strength concrete. [ABSTRACT FROM AUTHOR]

The issue of clogging in porous concrete (PC) pavements has been a significant concern. This study investigated the impact of various design porosities (20%, 25%, 30%) and different areas (commercial, traffic, residential, and park) on the clogging performance of recycled aggregate porous concrete (RAPC) using clogging materials. Image analysis and computer image processing techniques were utilized to examine the pore structure. Clogging tests were conducted using a custom-made permeability experimental apparatus to assess the changes in the permeability coefficient, permeability loss rate, and permeability recovery rate of RAPC. Additionally, a clogging model based on pore structure parameters was developed. The findings indicate a strong linear correlation between the permeability coefficient and RAPC planar porosity, perimeter, specific surface area, and average shape factor, as well as a quadratic polynomial relationship with composite pore structure parameters. The correlation coefficient exceeds 0.95. The permeability coefficient of RAPC decreases gradually with an increasing number of cloggings, while the recovery rate of the permeability coefficient initially decreases rapidly with an increasing number of backwashings before gradually stabilizing. The regression correlation coefficient exceeds 0.80, indicating that the RAPC clogging process follows an exponential decay pattern and can predict the permeability coefficient of RAPC after clogging. A prediction model for the RAPC permeability coefficient under cyclic clogging-rinsing was developed using surface regression analysis, yielding a coefficient of determination (R 2 ) of approximately 0.90. This study offers insights for addressing the clogging issue in RAPC. • Clogging in porous concrete: Impact of porosity & areas on performance. • Strong correlations: permeability-coefficient and pore structure parameters. • Development of a clogging model based on pore structure parameters. • Prediction model developed for RAPC permeability under clogging-rinsing. [ABSTRACT FROM AUTHOR]

Under heavy traffic loads, the aggregate skeleton characteristics of asphalt mixtures play a critical role. During asphalt mixture design, the maximum density line is conventionally used as a reference for selecting gradation. The selection of aggregate gradation is critical for asphalt mixtures to ensure good durability and mechanical performance. In this study, a 2-dimensional synthetic asphalt concrete section generation algorithm was developed to evaluate aggregate packing and interlock. During the synthetic section generation phase, an aggregate database consisting of real aggregate images was used. It was validated that synthetic sections with the desired gradation and area fraction (AF) could be generated using the developed method. Aggregate packing and interlock evaluation were conducted on synthetic sections generated for 10 different gradations using Bailey ratios and aggregate contact parameters. The results indicated that the developed methodology can be useful to assess the impacts of gradation changes on aggregate packing and interlock. The relationship between aggregate contact parameters and Bailey ratios was investigated, revealing a strong correlation between contact parameters and Fine Aggregate Coarse Ratio (FA c). The findings indicated that aggregates within the size ranges of the primary control sieve (PCS) and the secondary control sieve (SCS) had a significant impact on the aggregate interlock. • An algorithm was developed to generate 2D synthetic asphalt concrete sections. • Synthetic sections could be generated based on the desired gradation and AF. • Aggregate contact parameters and Bailey ratios were used for evaluating gradation. • Aggregate contact parameters correlated well with the FA c Ratio. • Aggregate interlock was significantly influenced by the aggregates in the range of PCS-SCS. [ABSTRACT FROM AUTHOR]

Strain-hardening cementitious composites (SHCC) are promising construction materials that enhance structural durability by forming multiple fine cracks under loading. Compared to conventional concrete, the cracks in SHCC are denser and tighter, making their quantification challenging. To thoroughly analyze and model the cracking behavior of SHCC, automatic algorithms with high accuracy are desired. In this paper, a workflow based on deep learning algorithms and 3D connectivity analysis for characterizing the crack formation and development process in SHCC under tensile loading is proposed. Temporal and spatial crack information, such as the crack number and individual crack width development during the loading process as well as the cracking strength distribution along the tensile specimens, can be obtained with minimal effort. The established workflow can provide more information for evaluating the overall cracking behavior of SHCC with high efficiency, to facilitate multiscale modeling and micromechanics-based design of the material. • A workflow for tracing individual tensile crack development in SHCC is established. • Temporal and spatial crack information analysis in SHCC tensile specimens is demonstrated. • The application of crack information in multiscale modeling of SHCC is highlighted. • Proposed workflow is suitable for diverse composite systems' cracking behaviors. [ABSTRACT FROM AUTHOR]

Wet-mix shotcrete, an essential form of concrete support, has been widely applied in underground constructions such as tunnels and mines. This study analyzed the gas jet structure and particle velocity differential equations in the wet-mix shotcrete process and physicochemically characterized collected dust samples. Based on field measurements and laboratory analyses, we established an experimental platform and a numerical simulation model for wet-mix shotcrete to investigate dust generation characteristics. The results showed the following: 1) The particles generated during wet-mix shotcrete primarily consisted of cement particles, exhibiting a spherical and blocky shape with a relatively regular morphology. However, their surfaces possessed numerous burrs and pores, resulting in poor wettability. 2) Suspended aerosol particles mainly comprised particles below 10 µm, exhibiting a unimodal particle size distribution. Smaller particles showed a higher abundance, with particles below 2 µm accounting for over 95% of the total particles. 3) The dust generation during wet-mix shotcrete was significantly influenced by the compressed gas flow rate and material viscosity. An increase in the gas flow rate or material viscosity led to higher production of fine particles. This study elaborated the mechanisms underlying particle generation during wet-mix shotcrete, providing valuable insights for wet-mix shotcrete dust control techniques. • Constructed a similar experimental platform for wet-mix shotcrete. • The number of particles below 2 µm in the wet-mix shotcrete process was relatively large. • The dust particles generated by wet-mix shotcrete were mostly spherical or blocky. • Dust generation zones were mainly at the boundary layer of the sprayed material and in the area of material rebounding. • The compressed gas flow rate and the material viscosity influence the wet-mix shotcrete dust generation. [ABSTRACT FROM AUTHOR]

Inadequate skid resistance (SR) performance will increase the risk and grade of traffic accidents. The structural stability related to the gradation may substantially affect the long-term skid resistance under the condition of high temperature and heavy load. This study characterized the mixture stability by coarse aggregate ratio (CA), coarse portion of fine aggregate (FA c), and fine portion of fine aggregate (FA f) in the Bailey method and the primary control sieve index and described the gradation integrity by Generalized Sigmodal model with five parameters. The 1/3 scale model mobile load simulator MMLS was used to simulate the unidirectional repeated heavy vehicle load at 60℃, then the effects of mixture gradation on the mean profile depth (MPD), texture level (L tx,0.5–20 mm), and evolution of friction coefficients were investigated. The evolution was explained from the perspective of mixture microstructure. It is found that although the gradation has little effect on the long-term friction measured by British Pendulum tester, the stronger skeleton formed by gradation can reduce the reduction ratio of the roughness and friction and has a significant positive effect on the long-term F 60 values. In addition, the Generalized Sigmodal model parameters for gradation are closely related to long-term skid resistance. The changing orientation and crushing of top layer aggregates causes the evolution of pavement roughness and friction under high temperature and heavy load conditions. This study will help actively intervene the SR performance of pavement during the mixture design to ensure driving safety during road service time. • The integrity and stability of gradation correlate with skid resistance. • Stronger aggregate skeleton reduces surface roughness and friction loss. • The rotation of aggregates and its breaking result in the friction evolution. • Generalized Sigmodal model fit the 0.45-power gradation curve well. [ABSTRACT FROM AUTHOR]

To reveal the microscale structural evolution characteristics of aggregates, this study employs confocal laser scanning microscope (CLSM) to conduct the surface microstructures of six aggregates: limestone, basalt, granite, diabase, sandstone, and recycled aggregate (RA). Quantitative analysis was performed on the surface morphology, roughness parameters, and material ratio of the aggregates. The results indicate that the surface features of the aggregates vary with the type and treatment method, and these distinctions may be attributed to variations in diagenetic conditions. In the original state, the surface texture of limestone, basalt, and diabase is relatively minimal, with arithmetic mean height (R a) distributed between 1.4 μm and 3.5 μm. Conversely, the surface texture of granite, sandstone, and RA is more complex, with R a distributed between 7.5 μm and 9.5 μm. The roughness indices of basalt, limestone, and diabase show an increasing trend after lime treatment, indicating a more pronounced presence of microscopic peaks and pits. In contrast, all roughness indices of granite, sandstone, and RA exhibit a declining after lime treatment. After polishing, the R a of the surfaces of the six different aggregates shows varying degrees of reduction. Specifically, R a decreased by 43.89%, 33.11%, 26.52%, 30.14%, 29.80%, and 38.79% for limestone, basalt, granite, diabase, sandstone, and RA, respectively. Furthermore, the height distribution frequency and material ratio of aggregate surfaces vary with the type and treatment of aggregates. Through grey correlation analysis, the correlation degrees of root mean square slope (R dq), kurtosis (R ku), and surface texture aspect ratio (R tr) are 0.9524, 0.9522, and 0.9514, respectively, belonging to hybrid parameter, height parameter, and spatial parameter. This suggests a close relationship between these parameters and the polished stone value (PSV) of the aggregates. These findings offer valuable insights for improving aggregate selection and treatment processes, potentially leading to enhanced durability and performance of construction materials. [Display omitted] • Aggregate type influences on surface morphology and surface roughness. • The treatment method affects the fractal dimension of aggregates. • The material ratio is related to the type of aggregates. [ABSTRACT FROM AUTHOR]