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Performing reliability analysis for reinforced concrete structures is a tedious and challenging task because it requires conducting a four-nested loop calculation procedure involving millions of data samples to account for the complex behaviours of the structures and multiple random variables. Therefore, the study proposes a novel, practical, and accurate reliability framework that is applicable for multi-component RC frame structures exhibiting different behaviours ranging from linear elastic and non-linear elastic to non-linear plastic. For this purpose, this study first employs a tree-based boosting ensemble model combined with quantile regression, dubbed as QR-LightGBM to calculate the structures' limit state function and the associated uncertainty estimation at the same time. Next, an active learning process is implemented to improve the computed reliability results progressively. During each active learning step, relevant data samples with potentially high impacts on the model accuracy are determined based on their uncertainty, and then QR-LightGBM is retrained utilizing these samples. By doing so, the prediction performance of the surrogate model is enhanced with a minimized number of actual data samples, thus significantly reducing overall computational resources. The viability and effectiveness of the proposed framework are validated through three case studies involving a simple 1D reinforced concrete beam, a 2D three-story frame, and a 3D five-story building structure. Furthermore, its performance is quantitatively demonstrated via comparison studies with competing methods such as Monte Carlo simulation, Kriging-based models, and an original LightGBM without active learning. [ABSTRACT FROM AUTHOR]

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed. Firstly, the overall scheme of this study is described. The CSA-based EMI damage technique method is theoretically presented by describing the behaviors of a CSA sensor embedded in a concrete structure under compressive loadings. The 2D CNN model is designed to learn and extract damage-sensitive features from a CSA's EMI responses to estimate stress and identify damage levels in a concrete structure. Secondly, a compression experiment on a CSA-embedded concrete cylinder is carried out, and the stress–damage EMI responses of a cylinder are recorded under different applied stress levels. Finally, the feasibility of the developed model is further investigated under the effect of noises and untrained data cases. The obtained results indicate that the developed 2D CNN model can simultaneously estimate stress and damage status in the concrete structure. [ABSTRACT FROM AUTHOR]

The coastal salt pan of Shijiazhuang-Hengshui-Cangzhou-Huanghua Port Intercity Railway project is a highly corrosive environment. In order to ensure the long-term operation safety of the bridge structure, it is urgent to carry out relevant durability research and improve the durability of the bridge structure in this project. Through detailed investigation and analysis of the erosive environmental characteristics of the bridge engineering in this region, the exposed environment of the bridge structure is evaluated, durability design of the bridge concrete structure is carried out, and the following research results are presented. In view of the corrosive environment problems faced by railway engineering, the principle of overall design scheme is proposed. The mix ratio design of bridge pier pile-cap and castin- place pile in coastal corrosive environment is conducted, and the preparation technology of high durability concrete suitable for coastal salt pan corrosive environment is formed. The structural design of durability is studied, structural design principle and physical isolation design concept are proposed, the structural design control measures are proposed to improve durability, and the structural physical isolation measures are designed to effectively improve the durability of bridge structure. In view of the erosion characteristics of the project, additional strengthened protection measures are put forward, including the silane impregnation protection and the application of corrosion resistant reinforcement. The permeable formwork cloth is studied and applied to improve the construction quality and compaction of concrete. An exposure test station is set up in the engineering site and a wireless monitoring system for reinforced concrete durability is developed. The whole process of reinforcement corrosion monitoring is realized through the wireless monitoring system. Through the durability design and research of concrete structure of bridge in the coastal environment with strong corrosion, the purpose of improving the durability of railway concrete structure under the harsh service condition of coastal salt pan has been achieved. At the same time, The solid foundation has been laid for the later evaluation of the durability of the bridge structure during the service period. [ABSTRACT FROM AUTHOR]

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The smelting process of Ferronickel in Indonesia produces a significant amount of waste in the form of Ferronickel Slag (FNS), with an annual accumulation of up to 13 million metric tons. Previous studies have shown promising strength results for concrete utilizing FNS as a fine aggregate. This study aims to analyze the mechanical properties of three reinforced concrete (RC) beams measuring 15 cm × 25 cm × 300 cm, each containing FNS as a 50% substitute for fine aggregate. The RC Beams underwent experimental testing using a four-point loading scheme under semi-cyclic loading conditions. Test results show the beams' capacity had reached up to 8 ton-f and their load–displacement responses show promising results. Digital Image Correlation (DIC) analysis facilitated the observation of surface deformation evolution due to loading, aiding in the identification of concrete crack patterns. Due to semi-cyclic loading, cracks on the beams' surface were experiencing a crack opening and closing phenomenon, where the propagations of cracks ceased or reclosed throughout the unloading process. Moreover, the opening of residual cracks was also captured by DIC analysis. The experimental finding was validated by finite element analysis. The RC beam numerical model was created using the Timoshenko Multi-fiber element in CAST3M software version 2022. Mazars concrete and elastoplastic steel damage model were used as constitutive laws for numerical modeling. The model's load–displacement response demonstrated satisfactory agreement compared to the experimental monotonic loading result. However, the model had limitations regarding the simulation of residual displacements of beams due to semi-cyclic loading. [ABSTRACT FROM AUTHOR]

Polypropylene fiber reinforcement is an effective method to enhance the durability of concrete structures. With the increasing public interest in the widespread use of polypropylene fiber reinforced concrete (PFRC), the necessity of evaluating the mechanism of polypropylene fiber (PF) on the permeability of concrete has become prominent. This paper describes the influence of PF on the concrete permeability exposed to freeze-thaw cycles under compressive and tensile stress. The permeability of PFRC under compressive and tensile loads is accurately measured by a specialized permeability setup. The permeability of PFRC under compressive and tensile loads, the volume change of PFRC under compressive load, and the relationship between compressive stress levels at minimum permeability and minimum volume points of PFRC are discussed. The results indicate that the addition of PF adversely affects the permeability of concrete without freeze-thaw damage and cracks. However, it decreases the permeability of concrete specimens exposed to freeze-thaw cycles and cracking. Under compressive load, the permeability of PFRC initially decreases slowly and follows by a significant increase as the compressive stress level increases. This phenomenon correlates with the volume change of the specimen. The compressive stress level of the minimum permeability point and compressive stress level of the minimum volume point of PFRC exhibit a linear correlation, with a fitted proportional function parameter γ ≈ 0.98872. Under tensile load, the permeability of PFRC increases gradually with radial deformation and follows by a significant increase. The strain-permeability curves of PFRC under loading are studied and consist of two stages. In stage I, the permeability of PFRC gradually decreases with the increase of strain under compressive load, while the permeability increases with the increase of strain under tensile load. In stage II, under compressive load, the permeability of PFRC increases with the increase of freeze-thaw cycles, whereas under tensile load, the permeability gradually decreases with the increase of freeze-thaw cycles. The reduction of PF on the permeability of PFRC under tensile load is greater than that under compressive load. In future research, the relationship between strain and permeability of PFRC can be integrated with its constitutive relationship between stress and strain to provide a reference for the application of PF in the waterproofing of concrete structures. [ABSTRACT FROM AUTHOR]

The safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. This algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set; then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. The maximum detection time is 366ms and the shortest is 213 ms. The successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value in engineering practice. This research has a broad development prospect. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The assessment of surface cracks in concrete structures plays a pivotal role in determining structural integrity. However, current diagnostic technologies suffer from drawbacks such as being time-consuming, subjective, and reliant on inspectors' experience, resulting in low detection accuracy. This paper seeks to address these issues by proposing an automated, vision-based method for identifying the surface condition of concrete structures. The method integrates advanced pre-trained convolutional neural networks (CNNs), transfer learning, and decision-level image fusion. To develop and validate this approach, a total of 6,500 image patches from diverse concrete surfaces were generated. Each pre-trained CNN establishes a predictive model for the initial diagnosis of surface conditions through transfer learning. Given the potential for conflicting results among different CNNs due to architectural differences, a modified Deep Belief CNN algorithm is crafted, thereby enhancing crack detection accuracy. The effectiveness of the proposed method is confirmed through a comparison with other CNN models. Robustness is tested by subjecting the method to images with various types and intensities of noise, yielding satisfactory outcomes. In practical scenarios, the hybridised approach is applied to analyse field-captured images of concrete structures using an exhaustive search-based scanning window. Results showcase the method's capacity to accurately identify crack profiles, with minimal areas of incorrect predictions underscoring its potential for practical applications.

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed. Firstly, the overall scheme of this study is described. The CSA-based EMI damage technique method is theoretically presented by describing the behaviors of a CSA sensor embedded in a concrete structure under compressive loadings. The 2D CNN model is designed to learn and extract damage-sensitive features from a CSA’s EMI responses to estimate stress and identify damage levels in a concrete structure. Secondly, a compression experiment on a CSA-embedded concrete cylinder is carried out, and the stress–damage EMI responses of a cylinder are recorded under different applied stress levels. Finally, the feasibility of the developed model is further investigated under the effect of noises and untrained data cases. The obtained results indicate that the developed 2D CNN model can simultaneously estimate stress and damage status in the concrete structure.

AbstractConcrete buildings constructed globally are susceptible to diverse circumstances of use and exposure to environmental elements. Diverse varieties of construction materials are used in the building sector to fabricate edifices and constructions. Architects and construction project managers use these classifications of materials and products to precisely determine the materials and techniques employed in building projects. This study presents a simulation of elastic guided waves in concrete structures, which is motivated by the vast variety of uses of such structures. The elastic-guided wave technique is a technology used for nondestructive assessment. This approach utilizes acoustic waves that travel along an extended structure, directed by its limits. This enables the propagation of waves over a significant distance while minimizing energy dissipation. Currently, the Elastic guided wave method is extensively used for the examination and evaluation of various engineering structures, namely for inspecting metallic pipelines globally. From a single place, it is possible to investigate distances of several hundred meters in some instances. Additionally, there are apps available for the examination of rail tracks, rods, and metal plate constructions. In this research, a novel mathematical model is introduced for accurately replicating elastic guided waves in concrete structures in practical applications. To reinforce the current concrete structure using mathematics, graphene oxide powders with improved mechanical properties are obtained using the role of the mixture, and the Halpin-Tsai micromechanical model is used. After obtaining the mathematical modeling of the current concrete structure using the theory of shear deformation with four unknown high-order terms and Hamilton’s principle, the equations are solved using free and forced wave propagation approaches. Finally, some applicable suggestions for improving the efficiency and dynamic stability of the presented concrete structure reinforced by graphene oxide powders are presented in detail in the results section. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Steel plate strengthening method is a common, efficient, and mature reinforcement method that utilizes structural adhesive to bond steel plates with concrete components to work together. It takes advantage of the excellent tensile strength of steel plates to improve the mechanical properties of the original structure. However, epoxy-based structural adhesives are highly sensitive to the environment, and curing effect at low temperatures is a key issue. This study developed a lowtemperature-resistant fast-curing structural adhesive (LTR-FCA) that can achieve rapid curing at low temperatures of -5 °C. The flexural performance of reinforced concrete beams strengthened with steel plates using LTR-FCA was compared with that using Normal Structural Adhesive (NSA). Experimental results indicated that LTR-FCA could rapidly cure at a low temperature of -5°C. Compared to NSA, which cures at room temperature, the use of LTR-FCA effectively delayed the debonding of the steel plates. [ABSTRACT FROM AUTHOR]

To study the distribution law of blasting fragmentation in the process of directional blasting demolition of concrete structure, a new method of the joint measurement method of mini-blasting I-type vibration meter and piezoelectric smart sensors was used to monitor the blasting load failure test of 30 specimens. Considering the influence of low frequency and high frequency in the measured signal, a smart damage determination method based on wavelet packet energy theory is established. The results show that the effects of concrete strength, blasting constraint face and water environment on blasting fragmentation distribution in the process of directional blasting demolition of concrete structures cannot be ignored. Under the saturated water environment, the dust pollution threshold of high-strength concrete structure is close to 5%, and the dust pollution threshold of low strength concrete structure is close to 6%. The joint measurement method of mini-blasting I-type vibration meter and piezoelectric smart sensors is feasible. In the analysis of blasting fragmentation, the quantitative analysis method of smart damage judgment based on wavelet packet energy theory is a practical new method of quantitative analysis of blasting fragmentation. [ABSTRACT FROM AUTHOR]

Bituminous substantial blends are the essential layer used commonly in Versatile pavements. The property of bituminous mixes can be improved by extension of fibers like coir strands. Fiber lengths were saved for instance 10mm, 15mm and 20mm and used at the speed of 0.3%, 0.5% and 0.7% by weight of mix. This paper gives a review of including Sisal fiber and Coir fiber as added substances in bituminous mix concrete. The utilization of sisal fiber moreover, coir fiber animates better, strength and impedes from channel down of the blend. The wire of fiber in the mix work on the mechanical properties. In India around 8 lakh heaps of coir fiber being made and used various outcomes, the extra waste fiber dumped in open land causing serious normal pollution. Along these lines, a survey has been embraced to involve the waste fiber as help in bituminous concrete. Sisal Fiber with low upkeep and irrelevantly extreme and recyclable mileage. The external forefront removes the sisal fiber that disposes of the crush from inside the plaid herringbone and fine fibers are open. Sisal is against static and will not easily ingest water or suddenness and doesn't attract dust particles or trap them. This paper shows the impact of sisal fiber with loop fiber on bituminous substantial blend. [ABSTRACT FROM AUTHOR]

Understanding the strain transfer mechanism is required to interpret strain sensing results for fiber optic cables. The strain transfer mechanism for fiber optic cables embedded in cementitious materials has yet to be thoroughly investigated experimentally. Interpretation of fiber optic sensing results is of particular concern when there is a displacement discontinuity. This study investigates the strain transfer mechanism for different types of fiber optic cables while embedded in concrete cubes, sustaining a boundary condition which features a displacement discontinuity. The strain transfer mechanisms for different cables are compared under increasing strain levels. Under cyclic loading, the nonlinear behavior of the force-displacement relation and of the strain distribution in the fiber optic cable are discussed. The mechanical properties of the fiber optic cables are presented and discussed. A parameter is proposed to quantify the strain transfer length. The results of this study will assist researchers and engineers to select appropriate cables for strain sensing and interpret the fiber optic sensing results.

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases.

The smelting process of Ferronickel in Indonesia produces a significant amount of waste in the form of Ferronickel Slag (FNS), with an annual accumulation of up to 13 million metric tons. Previous studies have shown promising strength results for concrete utilizing FNS as a fine aggregate. This study aims to analyze the mechanical properties of three reinforced concrete (RC) beams measuring 15 cm × 25 cm × 300 cm, each containing FNS as a 50% substitute for fine aggregate. The RC Beams underwent experimental testing using a four-point loading scheme under semi-cyclic loading conditions. Test results show the beams’ capacity had reached up to 8 ton-f and their load–displacement responses show promising results. Digital Image Correlation (DIC) analysis facilitated the observation of surface deformation evolution due to loading, aiding in the identification of concrete crack patterns. Due to semi-cyclic loading, cracks on the beams’ surface were experiencing a crack opening and closing phenomenon, where the propagations of cracks ceased or reclosed throughout the unloading process. Moreover, the opening of residual cracks was also captured by DIC analysis. The experimental finding was validated by finite element analysis. The RC beam numerical model was created using the Timoshenko Multi-fiber element in CAST3M software version 2022. Mazars concrete and elastoplastic steel damage model were used as constitutive laws for numerical modeling. The model’s load–displacement response demonstrated satisfactory agreement compared to the experimental monotonic loading result. However, the model had limitations regarding the simulation of residual displacements of beams due to semi-cyclic loading.

Polypropylene fiber reinforcement is an effective method to enhance the durability of concrete structures. With the increasing public interest in the widespread use of polypropylene fiber reinforced concrete (PFRC), the necessity of evaluating the mechanism of polypropylene fiber (PF) on the permeability of concrete has become prominent. This paper describes the influence of PF on the concrete permeability exposed to freeze-thaw cycles under compressive and tensile stress. The permeability of PFRC under compressive and tensile loads is accurately measured by a specialized permeability setup. The permeability of PFRC under compressive and tensile loads, the volume change of PFRC under compressive load, and the relationship between compressive stress levels at minimum permeability and minimum volume points of PFRC are discussed. The results indicate that the addition of PF adversely affects the permeability of concrete without freeze-thaw damage and cracks. However, it decreases the permeability of concrete specimens exposed to freeze-thaw cycles and cracking. Under compressive load, the permeability of PFRC initially decreases slowly and follows by a significant increase as the compressive stress level increases. This phenomenon correlates with the volume change of the specimen. The compressive stress level of the minimum permeability point and compressive stress level of the minimum volume point of PFRC exhibit a linear correlation, with a fitted proportional function parameter γ ≈ 0.98872. Under tensile load, the permeability of PFRC increases gradually with radial deformation and follows by a significant increase. The strain-permeability curves of PFRC under loading are studied and consist of two stages. In stage I, the permeability of PFRC gradually decreases with the increase of strain under compressive load, while the permeability increases with the increase of strain under tensile load. In stage II, under compressive load, the permeability of PFRC increases with the increase of freeze-thaw cycles, whereas under tensile load, the permeability gradually decreases with the increase of freeze-thaw cycles. The reduction of PF on the permeability of PFRC under tensile load is greater than that under compressive load. In future research, the relationship between strain and permeability of PFRC can be integrated with its constitutive relationship between stress and strain to provide a reference for the application of PF in the waterproofing of concrete structures.

A life cycle assessment (LCA) was utilised to evaluate the environmental impact of bacteria-based self-healing concretes (BBSHCs), where non-ureolytic bacterial endospores are encapsulated in porous calcium silicate granules. Findings reveal that 1 m3 of BBSHC has an overall 85% higher environmental impact than equivalent conventional concrete, primarily due to calcium nitrate and polyvinyl acetate. Furthermore, BBSHC has a 36% larger embodied carbon footprint (120 kg CO2 eq) and a 51% larger water footprint (260 L). However, by selectively incorporating BBSHC in specific areas of reinforced concrete structures, leveraging its inherent self-healing properties to deliberately allow wider crack widths, and consequently, reduce the amount of non-structural steel needed to control early-age cracking, sustainability improvements ranging from 12% to 50% can be achieved depending on the impact category. In this regard, a BBSHC-structure can potentially save up to 51 kg CO2 eq per m3.

Concrete is among the most widely used structural materials in buildings and bridges all over the world. During their service life, concrete structures may inevitably display cracks due to long-term fatigue loads, leading to the degradation of structural integrity. Thus, it is very important to detect cracks and their growth in concrete structures using an automated structural health monitoring system. In this paper, experimental research on crack detection and imaging of concrete structures by using sensing skin and electrical resistance tomography (ERT) is presented. Carbon ink is screen-printed on the surface of concrete as a conductive material to form sensing skins. With these sensing skins, when cracks occur on or near the surface, it breaks the continuity of the sensing skins and significantly reduces conductivity in cracking areas. Then, after exciting small currents in sensing skins and measuring related voltage data, an inverse analysis based on total variation (TV) regularization is adopted to reconstruct tomographic images showing conductivity changes in sensing skins, to detect the occurrence and growth of cracks. The effectiveness of conductive sensing skins and our related crack detection method is validated in experimental studies on a concrete beam subjected to fatigue tests. [ABSTRACT FROM AUTHOR]

Piezoelectric (PZT) sensors employed in the electro-mechanical impedance/admittance (EMI/EMA) technique are vulnerable to temperature variations when applied to concrete structural health monitoring (SHM). However, in practice, the ambient temperature transmitted from the air or surface to the concrete inner part is time-dependent during its monitoring process, which inflicts a critical challenge to ensure accurate signal processing for PZT sensors embedded inside the concrete. This paper numerically and experimentally investigated the thermal hysteresis effect on EMA-based concrete structure monitoring via an embedded PZT sensor. In the numerical modeling, a 3D finite element model of a concrete cube embedded with a PZT sensor was generated, where thermal hysteresis in the concrete, adhesive coat, and sensor was fully incorporated by introducing a temperature gradient. In the experiment, an equal-sized concrete cube installed with a cement-embedded PZT (CEP) sensor was cast and heated for 180 min at four temperature regimes for EMA monitoring. Experimental results, as a cogent validation of the simulation, indicated that EMA characteristics were functionally correlated to the dual effect of both heat transfer and the temperature regime. Moreover, a new approach relying on the frequency/magnitude of the maximum resonance peak in the EMA spectrum was proposed to effectively compensate for the thermal hysteresis effect, which could be regarded as a promising alternative for future applications. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses approaches to a digital transformation of the choice of technical solutions for restoring concrete bulk structures of hydraulic engineering structures (HES). The current state of the HES concrete structure is one of the most pressing issues of recent times. Particularly, choosing established technical solutions for restoring concrete is crucial. The imperfection of the regulatory and technical foundation is one of the most significant problems with the current state of the HES that can be resolved using digital transformation. The research led to the development of an algorithm for selecting technical solutions for restoring the bulk structure of HES concrete. [ABSTRACT FROM AUTHOR]

The torsional strengthening of reinforced concrete hollow beams (RCHBs) with externally bonded stainless steel wire mesh (SSWM) was investigated. The experimental programme involved casting and testing eight RCHBs of length 1300 mm and 150 mm × 150 mm cross-section with a 50 mm × 50 mm square hollow section in the centre. The beams were reinforced with 4–10 mm dia. longitudinal bars and 8 mm dia. stirrups at 150 mm centre to centre. Two unstrengthened RCHBs were designated as control specimens and the other six beams were strengthened using three different SSWM wrapping configurations. The test results were analysed based on torque–twist behaviour and the influence of SSWM on cracking torque, ultimate torque and failure modes. A numerical model was developed to predict the behaviour of RCHBs using the finite-element (FE) software Abaqus. The predictions of peak torque and corresponding twist obtained from the FE modelling showed good agreement with the experimental results. Based on the outcomes of this study, SSWM can be considered a feasible material for the torsional strengthening of reinforced concrete elements. [ABSTRACT FROM AUTHOR]

Concrete tends to develop cracks once damaged, and wide cracks specifically would affect the whole structure negatively. When used for repairing wide cracks, traditional cement-based grouting materials could no longer meet the demand for tensile strength. The paper aims to develop a high-performance grouting material for crack repair and further investigate its fluidity, and compressive strength, flexural strength, and splitting tensile strength after hardening. Based on the design of ultrahigh-performance concrete (UHPC), this grouting material consists of cement, sand, water, superplasticizer, expansion agent, silica fume, fly ash, and flat copper-plated steel fiber. The initial fluidity and 30-min fluidity of the grouting material decreased with the addition of fly ash, silica fume, and flat copper-plated steel fiber, among which the silica fume had the greatest influence on the fluidity, followed by fly ash and steel fiber. The optimal content of flat copper-plated steel fiber, fly ash, and silica fume was 2%, 10%, and 15%, respectively. Furthermore, the prediction model of compressive strength of grouting material was established. It indicated that cement grade, water-cement ratio, curing age, silica fume, fly ash, and flat copper-plated steel fiber and can be used for the mix proportion design of grouting materials. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Complete understanding of the adhesive curing effect of a bonded piezoelectric lead zirconate titanate (PZT) sensor on the electromechanical admittance (EMA, inverse of impedance) facilitates the accurate diagnosis of structural damages when employing the impedance technique for structural health monitoring. This article analytically and experimentally investigated the curing effect of adhesive layer on the EMA-based concrete structural damage detection. Curing effect was theoretically incorporated into shearing mechanism of adhesive layer to formulate a new one-dimensional PZT-structural impedance model. Analytical analysis of the curing effect on the EMA signature as well as the EMA-based damage diagnosis was calculably interpreted through the model. Proof-of-concept experiment was performed on testing a four-point bending reinforced concrete (RC) beam bonded with multiple PZT patches under adhesive curing. Experimental results demonstrated that singular shift in the EMA spectra under adhesive curing could be turned into three typical shifts when structural damage occurred, which cogently validated the analytical results. Quantifiable signal index further provided useful information for the identification of crack occurrence, progression, and reinforcement yielding of the RC beam. Results of this study potentially promoted the real-life engineering applications of impedance technique inclusive of deficient adhesive condition. [ABSTRACT FROM AUTHOR]

The cost of producing one cubic meter of concrete structure, depending on the structure's shape, type, and complexity, can be variously high. The cost of concrete, reinforcement, formwork, and labor ratio varies. But unlike the cost of concrete and reinforcement, which tend to be similar in the terms of the conditions of a particular construction project, the cost of formwork and work with it are different for different contractors. It often depends on the appropriateness of the formwork system used, the optimal placement of the formwork sets, the minimization of downtime, and the efficiency of the carpenters' work. Formwork modeling in the construction preparation phase intervenes in this planning area, where construction sources are defined and quantified, and the time course of their deployment is determined. The current paper deals with optimizing formwork selection and deployment in concrete structure execution. Even if several requirements must be considered when selecting and modeling the formwork (e.g., construction time, quality of concrete structure, etc.), an effort to minimize the cost of formwork, and thus construction cost, plays the most significant role. A dynamic model for effective and optimal planning of formwork in construction projects, including formwork cost analysis, is presented in the paper. The included case study demonstrated the planning of the formwork through a software application developed based on the computational algorithm of the dynamic model presented. A case study is presented in the article as a research method. An office building with five above-ground floors and one underground floor was chosen for the case study. To solve the case study, the formwork for horizontal structures (i.e., beams and slabs) is considered. The goal of the case study is to identify research questions and apply time and cost optimization to a selected specific building. [ABSTRACT FROM AUTHOR]

Urban overpasses, being long-span linear projects, are inevitably affected by ground fissure activities. This study focused on the three-span continuous overpass, and used the MIDAS GTS NX finite element simulation software to establish the model of an urban overpass crossing active ground fissures with different straddling ground fissure positions and hanging wall settlements. This study analyzed the deformation and stress characteristics of soil and bridges induced by ground fissures under different working conditions. The results demonstrate that the soil's displacement response is mainly concentrated near the ground fissures, and the displacement changes decrease with increasing ground fissures. Ground fissure activities redistribute the stress inside the beam and pier, resulting in the tilting of the pier. When the fissure activity is small, the use of multi-span beams to straddle the ground fissures can effectively reduce the influence of ground fissure activities on bridge deformation. However, when the fissure activity is significant, the bridge may be damaged, and excessive deformation of the bridge and piers may not meet code requirements. In such cases, the multi-span beam structure type should be changed to adapt to the ground fissure activity. The research results provide significant reference value for urban traffic planning and overpass design. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nondestructive inner inspection techniques are essential to certify the safety or health of concrete structures, and one of the important issues in these inspections is the detectability of thin air voids or cracks. In this study, we propose a novel method for discriminating the variety and estimating the thickness of air voids based on the microwave radar technique. Specifically, to distinguish air voids from other objects, the phase shift of the microwaves was analysed using time-frequency analysis. In addition, based on a simple parallel incidence model, microwave reflection and refraction analysis were conducted on homogeneous layers, and a particular relation was calculated between the reflectance and the air void thickness. The experimental performance of the functions to distinguish air voids was good, however, quantitative thickness estimation failed. Therefore, we introduced a novel sophisticated propagation path model based on ray-traced propagation, which provides a couple of propagation paths. Additionally, the attenuation feature of microwaves was also considered. A quantitative thickness estimation method was re-applied to the experimentally observed waveforms. The estimation accuracy improved by approximately 54%, and the validity of quantitative thickness estimation was confirmed.

The durability of concrete structures is often reduced owing to the corrosion of reinforcement in an aggressive environment. Ordinary reinforcement methods, such as wrapping section steel or steel plate, are also vulnerable to corrosion. Using 6061-T6 aluminium alloy as near-surface reinforcement of the concrete structure is a feasible method. In this study, the corrosion resistance of 6061-T6 aluminium alloy bars was studied by simulating the coastal environment, atmospheric environment, and concrete internal environment with chloride solution, simulated acid rain solution, and saturated Ca(OH)2 solution. The corrosion rate of the 6061-T6 aluminium alloy in the above environments was tested using a weight loss method, and its corrosion resistance was evaluated using the metal corrosion resistance classification standard. Based on the electrochemical reaction mechanism, the polarisation properties and AC impedance spectra of steel and 6061-T6 aluminium alloy were compared, and the corrosion resistance mechanisms of steel and the 6061-T6 aluminium alloy in the above corrosive environments were obtained. The results show that the 6061-T6 aluminium alloy has better corrosion resistance than steel bars in chloride and atmospheric environments, with corrosion currents of 0.012 and 0.037 µA·cm−2, and 8-day corrosion rates of 0.051 and 0.031 mm·a−1, respectively. However, owing to the activity of the aluminium alloy, its corrosion resistance in an alkaline environment inside concrete is poor; the corrosion current is 0.22 µA·cm−2 and the 8-day corrosion rate is 16.166 mm·a−1. The research results can provide a reference for applying aluminium alloy bars as external prestressed concrete bars and near-surface steel bars.

Copyright of New Building Materials / Xinxing Jianzhu Cailiao is the property of New Building Materials Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A historical overview of the issues of reliability and engineering safety of hydraulic structures is given. Most of the large hydroelectric facilities were put into operation in the middle of the last century. The results of an integrated survey of reinforced-concrete structures of the Zhigulevsk Hydroelectric Power Plant after a half-century period of operation are discussed. The results of technical evaluation of hydraulic structures, including HPP buildings, flow paths of hydraulic units, spillway dam, are presented. [ABSTRACT FROM AUTHOR]

Based on the extensive use prospect of acoustic emission technology in concrete damage detection, researches on acoustic emission analysis methods of concrete damage at home and abroad were analyzed. The sorting of acoustic emission analysis methods for concrete damage was focused on four aspects: characteristic parameter analysis of acoustic emission signal of concrete damage, waveform analysis of acoustic emission signal, concrete damage location analysis based on acoustic emission technology, and application of acoustic emission technology for concrete structure damage under fire. The research status of various acoustic emission analysis methods was introduced, and their technical characteristics and application scope were summarized. [ABSTRACT FROM AUTHOR]

In order to further study the new floor structure of honeycomb concrete open-web sandwich plate and improve the openweb sandwich plate structure system, based on the concrete honeycomb open-web sandwich plate floor of a university sports center, the Y-shaped shear keys were extracted for model simplification and establishes a finite element benchmark model. Then, through parametric analysis, the change of static performance of Y-shaped shear key under the influence of different factors was studied. The results show that the ratio of longitudinal reinforcement has little influence on the shear bearing capacity of the shear key, but the ratio of stirrup volume, the strength of concrete and the cross-sectional area of the shear key have a great influence on it. It provides a certain reference value for the design and research of concrete honeycomb open-web sandwich plate. [ABSTRACT FROM AUTHOR]

Most concrete structures suffer from degradation, where cracks are the most obvious visual sign. Concrete structures must be continuously monitored and assessed to avoid further deterioration, which may lead to a partial or total collapse. This is particularly important when constructing large structures such as towers, bridges, tunnels, and dams. This work aims to demonstrate and evaluate several deep learning approaches that can be used for monitoring and assessing the level of concrete degradation based on the cracks' visual signs, which can then be embedded in Health Monitoring Systems (SHM). The experimental work in this study involves creating three models: Two were built using ResNet-50 and Xception transfer learning networks. In contrast, the third was built using a customized Sequential Convolutional Neural Network (SCNN) architecture. The dataset comprises 2,000 image samples sampled from a larger dataset that contains 56,000 images and which belong to four severity classes: minor, moderate, and severe, in addition to a normal class for no crack signs. The SCNN model achieved an accuracy of 90.2%, while the Xception and ResNet-50 models scored an accuracy of 86.3% and 70%, respectively. [ABSTRACT FROM AUTHOR]

In this study, the deformation of concrete materials was evaluated using a mechanochromic sensor that detects the discoloration reaction caused by deformation. This sensor was attached by applying the Loctite adhesive to both ends in the longitudinal direction. The process of applying tensile stress to the specimens was videotaped, and the deformation and discoloration were examined through image analysis. The mechanochromic sensor was not affected by the finished surface condition, and the discoloration reaction was detected for a concrete material deformation level of up to 0.01 mm. The detected level was caused by the elongation of the sensor, and the discoloration compared with the initial color was identified. In addition, the integration behavior of the mechanochromic sensor under the deterioration of concrete members in cold areas and winter environments, as well as the discoloration reaction of the sensor in a low-temperature environment, was examined. It was found that the discoloration ability of the mechanochromic sensor exposed to a low-temperature environment was restored in 2 h after the end of the freeze–thaw test, and it was judged that the deformation and discoloration levels will be properly measured when the surface temperature of the sensor is restored to a room temperature of approximately 15 °C. This appeared to be due to the room temperature recovery of the dielectric spacer of the sensor and the deformation structure of the resonance condition. The sensor was also attached when diagonal cracks occurred in the concrete beam members to evaluate the strain and discoloration rate according to the deformation and discoloration levels. Accordingly, the cracks and deformation of the concrete materials were monitored using measured values from the discoloration of the mechanochromic sensors, and the possibility of measuring the crack width was reviewed only by real-time monitoring and imaging with the naked eye. [ABSTRACT FROM AUTHOR]

Concrete cracks have always been the focus of research because of the serious damage they cause to structures. With the updating of hardware and algorithms, the detection of concrete structure surface cracks based on computer vision has received extensive attention. This paper proposes an improved algorithm based on the open-source model Deeplabv3+ and names it Deeplabv3+ BDF according to the optimization strategy used. Deeplabv3+ BDF first replaces the original backbone Xception with MobileNetv2 and further replaces all standard convolutions with depthwise separable convolutions (DSC) to achieve a light weight. The feature map of a shallow convolution layer is additionally fused to improve the detail segmentation effect. A new strategy is proposed, which is different from the two-stage training. The model training is carried out in the order of transfer learning, coarse-annotation training and fine-annotation training. The comparative test results show that Deeplabv3+ BDF showed good performance in the validation set and achieved the highest mIoU and detection efficiency, reaching real-time and accurate detection. [ABSTRACT FROM AUTHOR]

Additional strain increments occur in concrete subject to stress constraints during cold shrinkage, resulting in irregular deformation and reducing the concrete structure's stability. When an annular concrete structure is subjected to radial pressure, two tensile stress concentration zones will appear at the intersection of the inner wall and the diameter along the pressure direction. When exposed to low temperatures, the total strain in the tensile stress concentration zones is caused by the combined effect of applied stress strain and thermal strain. Then, the thermal strain of the structure can be obtained from the difference between the total strain and the applied stress strain. Gradient cooling was performed after applying radial pressure to the annular concrete using a counterforce device. The applied stress strain and total strain of the tensile stress concentration zones are measured by fiber Bragg grating (FBG) strain sensors fixed along the stress direction. According to the measurement results, the thermal strains of the concrete structure under the stress constraint are extracted to analyze the influence of the tensile stress constraint on the thermal strain of the concrete structure. In the temperature range of − 40 ∘ C ∼ 20 ∘ C , the thermal strains of the structure under radial pressures of 1500 N, 2000 N, and 3000 N are extracted, respectively. The thermal expansion coefficients are calculated based on the thermal strain of the structure. The free thermal expansion coefficient of concrete structures fluctuates around 11 × 10 − 6 / ∘ C . When the temperature is reduced to 10 ∘ C , the difference between the thermal expansion coefficient under the stress constraint and the free thermal expansion coefficient is the largest. When the temperature is reduced to − 20 ∘ C , the thermal expansion coefficients under each stress condition are close to the same. The results show that the stress confinement significantly inhibits the cold shrinkage of the concrete structure, and the inhibitory effect is gradually weakened when the temperature decreases. [ABSTRACT FROM AUTHOR]

Water that penetrates through cracks in concrete can corrode steel bars. There is a need for reliable and practical seepage sensing technology to prevent failure and determine the necessary maintenance for a concrete structure. Therefore, we propose a modified plasma-assisted electrochemical exfoliated graphite (MPGE) nanosheet smart tag. We conducted a comparative study of standard and modified RFID smart tags with sensor technology for seepage detection in concrete. The performance of both smart tags was tested and verified for seepage sensing in concrete, characterized by sensor code and frequency values. Seepage was simulated by cracking the concrete samples, immersing them for a designated time, and repeating the immersing phase with increasing durations. The test showed that the modified smart tag with 3% MPGE and an additional crosslinking agent provided the best sensitivity compared with the other nanosheet compositions. The presence of 3D segregated structures on the smart tag's sensing area successfully enhanced the sensitivity performance of seepage detection in concrete structures and is expected to benefit structural health monitoring as a novel non-destructive test method. [ABSTRACT FROM AUTHOR]