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Steel corrosion is by far the biggest durability issue for many reinforced concrete bridge structures worldwide. The columns in these bridges can be critical, especially for seismic resistance. In this study, glass-fibre-reinforced-polymer bars were investigated as an alternative to steel for sustainable and resilient bridge construction. As lateral reinforcement is more susceptible to corrosion, and structures fully reinforced with fibreglass bars display softer responses with lower shear and flexural capacity, fibreglass spirals and longitudinal steel reinforcement were used in the tested columns. The experimental programme included the design, construction and seismic testing of full-scale (508 mm diameter) and half-scale (356 mm diameter) columns. The other variables investigated were the axial load and the amount and spacing of spirals. For both column sizes, fibreglass spirals provided increasing confining pressure to the concrete core with increased deformations. For the full-scale columns, no adverse size effects were observed in comparison to the half-scale columns. The columns produced large ductility and energy dissipation. Results from selected representative specimens are presented here to establish the feasibility of using fibreglass spirals in circular bridge columns. Resilience can thus be built into new columns through innovative durable fibreglass lateral reinforcement, with similar if not superior behaviour to steel-reinforced columns. [ABSTRACT FROM AUTHOR]

Soil–structure interaction can significantly affect the seismic vulnerability of systems through several mechanisms that depend on the mutual effects of the soil properties and the structural characteristics. In this work, a probabilistic-based approach was applied to evaluate such effects on the fragility of an isolated bridge representing a typical Californian highway bridge. Analytical fragility curves were developed using OpenSees, implementing hysteretic materials and advanced plasticity models to represent non-linearity. The mutual non-linear effects of soil deformability and isolation on the system (soil–foundation–structure) were assessed by considering several limit states for the column ductility and the deck displacement. [ABSTRACT FROM AUTHOR]

The growing threat of accidental and malevolent blast loading to building structures has necessitated the consideration of blast-resistant design. Columns are the most vulnerable and critical component of a building, so they must be designed against blast to achieve defined performance goals. The influence of transverse rebar detailing on the ultimate capacity and failure modes of rectangular reinforced concrete (RC) columns against blast loads was investigated under different detonation scenarios. The performance of the columns was quantified using the failure charge mass and post-blast residual axial capacity. Pressure–impulse diagrams were used to study the failure modes. The analyses showed that special transverse detailing measures were the most effective for columns subjected to near-field surface bursts. The confinement of concrete due to ties and axial restraint played a critical role in improving the blast performance of columns. The provision of diagonal rebar improved the blast resistance by preventing direct shear failure, and was most effective when coupled with closely spaced lateral ties. Finally, the performance of code-compliant columns against blast was studied to determine the blast safety margin and the scenarios under which it would be necessary to undertake blast retrofitting of the columns in existing critical buildings. [ABSTRACT FROM AUTHOR]

The eccentric load behaviour of circular reinforced concrete columns strengthened with a novel engineered cementitious composite jackets reinforced with synthetic macro-fibres and polypropylene was investigated. Furthermore, an innovative method of interface treatment known as longitudinal grooving was used. Twelve test columns were cast, six of which were strengthened with 15 mm thick composite jackets, while three were strengthened with ultra-high-performance fibre-reinforced concrete jackets. In addition, three of the six columns strengthened with the composite jackets were confined with glass-fibre-reinforced polymer warps. The experimental results showed that both methods enhanced load-carrying capacity and energy dissipation with increasing load eccentricity. For example, the specimens strengthened with the composite jackets exhibited increases of 167% and 194% in load-carrying capacity, and enhancements of 92% and 53% at eccentricities of 30 and 60 mm, respectively. Finally, the previously reported model and different codes were used to validate the results, from which interaction diagrams were drawn. This showed a good agreement between experimental and predicted results. [ABSTRACT FROM AUTHOR]

The results of an experimental investigation of a new dry connection for reinforced concrete slab elements are presented. Seven full-scale slabs were tested; one slab was monolithic and used as a control specimen, while the other six were assembled using top and bottom steel plates joined by steel bolts with high tensile strength. Two connection scenarios were investigated – a simple bolted connection and a connection with a shear key. The parameters studied were the use of stirrups at the connection section, the step size of the shear key, the bolt diameter and the number of bolts. The test results showed that using a shear key at the assembled section in demountable slabs was more efficient than the simple bolted connection, providing higher flexural stiffness, greater load capacity and less deflection. However, increasing the shear key step size improved the flexural performance of the demountable slabs. In addition, adding stirrups in the assembled section enhanced the flexural stiffness and the total load capacity of the demountable slabs. Predictions of the moment capacity and deflection of the demountable slabs showed reasonably good agreement with the experimental results but, to be generalised, additional calibrated data from experiments are required. [ABSTRACT FROM AUTHOR]

This research examines the influence of high-volume fly ash (HVFA) on reinforced concrete (RC) beam deflections over 180 days of continuous loading. The study comprises eight fly ash-based concrete mixes with varying fly ash ratios (0–60%). Experimental assessments were conducted on two load levels, corresponding to 25% and 50% of the initial cracking load. Thirty-two full-scale RC beams, each measuring 100 × 150 × 1800 mm, were fabricated for both load levels. Long-term deflections were monitored for all concrete mixes through sustained four-point bending tests at ages ranging from 1 to 180 days. A comparative analysis of the overall mid-span deflection between conventional and fly ash concrete RC beams was carried out against established design codes. It was determined that high-volume fly ash RC beams exhibited less creep and shrinkage deflection compared with conventional RC beams. Moreover, artificial neural network (ANN) modelling was also carried out to propose a significant model helpful in predicting the total deflection of RC beams containing any fly ash content and at any age of concrete. The present study offers valuable insights for engineers and designers, facilitating the evaluation of age-dependent deflection patterns in RC beams constructed with reinforced high-volume fly ash concrete. [ABSTRACT FROM AUTHOR]

Concrete structures frequently manifest diverse defects throughout their manufacturing and usage processes due to factors such as design, construction, environmental conditions and distress mechanisms. In this paper, a multilevel convolutional neural network (CNN) combined with array ultrasonic testing (AUT) is proposed for identifying the locations of hole defects in concrete structures. By refining the detection area layer by layer, AUT is used to collect ultrasonic signals containing hole defect information, and the original echo signal is input to CNN for the classification of hole locations. The advantage of the proposed method is that the corresponding defect location information can be obtained directly from the input ultrasonic signal without manual discrimination. It effectively addresses the issue of traditional methods being insufficiently accurate when dealing with complex structures or hidden defects. The analysis process is as follows. First, COMSOL-Multiphysics finite element software is utilized to simulate the AUT detection process and generate a large amount of ultrasonic echo data. Next, the extracted signal data are trained and learned using the proposed multilevel CNN approach to achieve progressive localization of internal structural defects. Afterwards, a comparative analysis is conducted between the proposed multilevel CNN method and traditional CNN approaches. The results show that the defect localization accuracy of the proposed multilevel CNN approach improved from 85.38% to 95.27% compared to traditional CNN methods. Furthermore, the computation time required for this process is reduced, indicating that the method not only achieves higher recognition precision but also operates with greater efficiency. Finally, a simple experimental verification is conducted; the results show that this method has strong robustness in recognizing noisy ultrasonic signals, provides effective solutions, and can be used as a reference for future defect detection. [ABSTRACT FROM AUTHOR]

Corrosion of steel rebar in reinforced concrete (RC) constructions is an issue of great concern toward ensuring the integrity of civil structures, and thus, detection of the on-set of corrosion process, if any, will tremendously help in taking timely and effective measures. To address this issue, the current work aims to evaluate the potential of using different nonlinear ultrasonic (NLU) methods to detect corrosion through analysis of the signal characteristics from both time and frequency domain. Three NLU parameters (from time and frequency domain response) are studied by using an indirect type of measurement obtained from RC specimens undergoing accelerated corrosion process. Signal subtraction method (SSM)-based time domain nonlinear parameter and sideband peak count (SPC)-based frequency domain nonlinear parameter are found to be effective in detecting the specific damage stages during the corrosion process. There exists a significant shift in the cumulative nonlinear parameter curve arrived based on SSM when the mass loss is around 14 g, corresponding to the time at which the first surface crack of about 0.2 mm width appeared. Cumulative nonlinear parameter curve arrived based on SPC, shows a drastic shift when the mass loss is only 2 g, i.e., at the time when microcrack initiates inside concrete. To provide a complete monitoring solution, the complementary abilities of both time and frequency domain NLUs are used for the first time to formulate a damage index that can effectively monitor the progression of damage caused by corrosion from the beginning of the process until the point of failure. [ABSTRACT FROM AUTHOR]

BIM‐based assessment of sustainable maintenance measures for existing building structures In Germany, around 70 % of all building construction work is done on existing buildings. This high proportion emphasizes the need for the maintenance management of buildings. For this purpose, digital methods and technologies are combined with integrating sustainability aspects for optimized decision‐making. In this paper, a holistic method for the building maintenance is developed by using the BIM‐method in combination with the integration of sustainability aspects. Different digital technologies are combined within a semi‐automated workflow. In the first step, the status quo of digital maintenance on reinforced concrete structures is analyzed in a literature review. Subsequently, the process‐related and data‐technical basics are defined. Based on this, the concept development and validation are carried out by holding the steps ‐ data acquisition, an algorithmic data process and sustainability analysis as well as data provision via augmented reality and a common data environment. At the end, the process steps are validated using a real demonstrator. The result of the article demonstrates a semi‐automated process chain and decision support for resource‐efficient maintenance management of reinforced concrete structures. [ABSTRACT FROM AUTHOR]

Most design codes evaluate the non-linear seismic performance of structures using the response reduction/modification factor (R). The value of R is sensitive to a variety of factors in terms of overall ductility and over-strength. In this work, actual R values were assessed for vertical irregularity cases for reinforced concrete bare buildings with moment-resisting frames (MRFs). A significant relationship between R and the vertical irregularity index, calculated from the relative stiffness between adjacent storeys, was derived. Three-dimensional numerical modelling was carried out for soft storey and setback irregularity scenarios using Etabs. Modal pushover analysis was used to obtain the inelastic seismic capacity. It was found that vertically irregular buildings have weak inelastic seismic capacities compared with regular buildings. So, before the design stage, R should be scaled down by 15–40% for single and combined vertical irregularity scenarios. Structures with a combined asymmetric setback with a soft ground storey were found to have the worst R value and R was sensitive to the vertical irregularity index (Vtm) that has R-squared of 80%. So, the vertical irregularity index can be used to specify the allowable vertical irregularity ratio, location and combination of vertical irregularities for each seismic zone. [ABSTRACT FROM AUTHOR]

Shape memory alloys (SMAs) are metallic materials that are characterized by their ability to restore their original shape after large deformation when activated by heating. This unique property renders SMAs appealing for various civil engineering applications. Iron-based SMAs (Fe-SMAs), including alloys like Fe–Mn–Si, stand out due to their cost-effectiveness and high strength. The primary focus of this research lies in the computational modeling of Fe-SMA strips utilized to reinforce damaged concrete structures. To achieve this, details from an experimental test are leveraged for the computational simulation of real-scale reinforced concrete beams that were first loaded to some level of damage, then released and strengthened, and subsequently retested. The strengthening approach involves the application of external Fe-SMA strips wrapping around the beams. This paper presents an original computational modeling setup that incorporates a switch option for the Fe-SMA material. This feature enables one to use a single simulation platform for the whole process. The significance of this method originates from its capacity to ensure a robust analysis that includes all simulation steps-testing unstrengthened beams, installing and heating Fe-SMA strips, and testing both damaged and strengthened beams—in a single, multi-step analysis. The computational simulation results were compared with the outcomes of the experimental test, revealing an acceptable level of agreement. The findings indicate a substantial increase in both shear strength and ductility as a result of the application of Fe-SMA strips. Additionally, parametric and mesh sensitivity studies were conducted. These aimed to investigate the mesh dependency of the model and to identify the optimal mesh size. Furthermore, variations in the details of the Fe-SMA strips, including thickness, width, quantity, and effect of applied temperature were explored to compare the outcomes of different applications of these strips. [ABSTRACT FROM AUTHOR]

This document provides a comprehensive overview of the integration of civil structural health monitoring and machine learning in the field of concrete structures. It discusses the importance of monitoring infrastructure condition and the challenges of manual inspection. The document highlights the increasing use of machine learning algorithms, such as computer vision methods, in optimizing maintenance and repairs. It presents case studies and applications of machine learning in civil engineering, including damage detection and load assessment. The document also emphasizes the challenges and limitations of using machine learning in this context and recommends further research and interdisciplinary collaboration. Additionally, it addresses ethical and privacy concerns, the importance of open data sharing, and aligning machine learning applications with sustainability efforts. The document includes a list of academic articles related to machine learning techniques in concrete structures, covering topics such as crack detection and structural health monitoring. It also compiles various studies and research papers on machine learning for structural health monitoring in concrete structures, exploring different methods and highlighting the potential of deep learning models and image processing techniques for crack detection. Overall, the document provides a comprehensive overview of the application of machine learning in the field of structural health monitoring for concrete structures. [Extracted from the article]

Corrosion in reinforced concrete structures is among the major degradation mechanisms. The quantification and description of the spatial distribution of the corrosion condition within a structure on the basis of condition assessments are important. This study considered half-cell potential mapping data as a widely used technique to detect corrosion in reinforced concrete structures. A four-step workflow was proposed to analyse half-cell potential data with geostatistical techniques, first consisting of trend identification and possible trend removal. The obtained residuals were then subjected to a quantile-quantile transformation. Subsequently, experimental variograms were calculated and fitted with variogram models to estimate the correlation lengths. A case study with data from a road tunnel confirms the applicability of the workflow. It was assumed that the identified trend is primarily a result of the heterogeneity of the exposure conditions within the structure that ranges several metres. The residuals are interpreted as the result of the heterogeneities of material resistances that give rise to spatial variability in corrosion probability on a shorter distance range. The proposed analysis may be utilised for service life modelling (e.g. based on random fields), planning maintenance works, or optimising the grid size for half-cell potential measurements. [ABSTRACT FROM AUTHOR]

Steel corrosion is one of the main factors limiting the durability of existing reinforced concrete components. Based on the corrosive circumstances, different prevention and repair methods can be applied. In the case of carbonation-initiated corrosion, chemical re-alkalisation (CRA) is one of the standard measures to counteract alteration processes in the concrete that lead to reinforcement corrosion. The current state of research on the CRA of carbonated concrete and the various materials that have been investigated in terms of their applicability for this repair measure are summarised. The review summarises current research results in the field of carbonated concrete and the proven pozzolanic reactivity of the carbonation products in contact with portlandite, which suggests an inhibitory effect on CRA, although this has not yet been investigated. There are also initial attempts that deviate from the application of classic Portland cement as a repair material, but their applicability in practice must be questioned. The use of alternative binders and the associated different compositions of pore solutions and varying alkalinity of these binders (e.g. composite cements with a high substitution rate or alkali-activated binders), have not yet been investigated for repair processes such as CRA, although the chemical composition of binders is of the greatest importance for successful application of the repair measure. [ABSTRACT FROM AUTHOR]

Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC's engineering properties (i.e., Poisson's Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC's engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders. [ABSTRACT FROM AUTHOR]

In reinforced concrete (RC) buildings, the seismic behaviour of RC beams with flush outside faces of beams and columns has not been verified by experiments. In this study, four large RC beam specimens were fabricated and an experimental verification was conducted. The results showed that all of the specimens (including three beam specimens with flush outside faces of beams and columns) could withstand ultimate drift angles up to 4% rad. In addition, the lateral strength and seismic behaviour of a specimen with a concrete cover of 85 mm were no less than those of the benchmark specimen, so a concrete cover thickness of 85–40 mm should be acceptable for RC beams. The seismic behaviour of two specimens with lateral U-shaped auxiliary transverse reinforcement and a concrete cover of 85 mm was better than that of the benchmark specimen, but the crack width of the latter was wider and the spalling degree of the concrete cover was more serious. Based on the results of this work, the practical recommendation is to adopt lateral U-shaped auxiliary transverse reinforcement in which the two legs extend beyond half the width of the hoop, with two auxiliary longitudinal reinforcements and the same spacing as the hoop. [ABSTRACT FROM AUTHOR]

Structural health management constitutes an essential factor in ensuring the durability of concrete structures. Cracks in concrete, reinforcement corrosion, alkali–silica reaction and efflorescence attacks are common concrete defects that can be identified visually. However, the detection and classification of these defects in concrete bridges and other high-rise concrete structures is a difficult and expensive process using manual approaches. In this research, a deep learning application is applied to detect and classify concrete defects. Concrete images from the public repository were used to create the explored database. The database was divided into training and validation subsets. The visual geometry group (Vgg19), neural search architecture (nasnetlarge) and residual inception block (vinceptionresnetv2) algorithms were used to analyse the images. The results of the overall performance show that the Vgg19 algorithm recorded higher accuracy in the detection and classification of concrete defects when compared to the nasnetlarge and inceptionresnetv2 algorithms. The efficiency of the proposed approach was evaluated using a new dataset containing images of concrete defects. The outcome of this research shows clearly that deep learning models could enhance the efficiency of concrete structural health monitoring in a multi-classification scenario. [ABSTRACT FROM AUTHOR]

In reinforced concrete (RC) buildings, the seismic behaviour of RC beams with flush outside faces of beams and columns has not been verified by experiments. In this study, four large RC beam specimens were fabricated and an experimental verification was conducted. The results showed that all of the specimens (including three beam specimens with flush outside faces of beams and columns) could withstand ultimate drift angles up to 4% rad. In addition, the lateral strength and seismic behaviour of a specimen with a concrete cover of 85 mm were no less than those of the benchmark specimen, so a concrete cover thickness of 85–40 mm should be acceptable for RC beams. The seismic behaviour of two specimens with lateral U-shaped auxiliary transverse reinforcement and a concrete cover of 85 mm was better than that of the benchmark specimen, but the crack width of the latter was wider and the spalling degree of the concrete cover was more serious. Based on the results of this work, the practical recommendation is to adopt lateral U-shaped auxiliary transverse reinforcement in which the two legs extend beyond half the width of the hoop, with two auxiliary longitudinal reinforcements and the same spacing as the hoop. [ABSTRACT FROM AUTHOR]

This research introduces a novel approach for detecting defects in concrete structures. It utilizes the Gramian Angular Difference Field (GADF) in combination with a Convolutional Neural Network (CNN) enhanced by depthwise separable convolutions and attention mechanisms. The key contribution of this work is the use of GADF to transform one-dimensional impact-echo signals into two-dimensional images, thereby improving feature extraction and computational efficiency for analysis by the CNN. This advancement offers a new perspective in non-destructive testing technologies for concrete infrastructure. Comprehensive evaluation on a varied dataset of concrete structural defects reveals that our GADF-CNN model achieves an impressive test accuracy of 98.24 %, surpassing conventional models like VGG16, ResNet18, DenseNet, and ResNeXt50, and excelling in precision, recall, and F1-score metrics. Ultimately, this study enhances the integration of sophisticated image transformation techniques with deep learning, contributing to safer and more durable concrete infrastructure, and represents a noteworthy development in the field. [ABSTRACT FROM AUTHOR]

In this study, comprehensive analytical and parametric studies were conducted on seismic performance of concrete structures equipped with self-centered unbonded post-tensioned hybrid wall system in comparison with traditional structural wall system. For this purpose, analytical models were developed and to further study the seismic performance of self centering hybrid wall, parametric analysis was performed considering four parameters: wall aspect ratio, initial stress of post-tensioned (PT) tendons, the total area of PT tendons, the total area of mild bars. The performance of buildings was evaluated under non-linear quasi cyclic and time history method considering two seismic hazard levels, the design-based earthquake (DBE) level and the risk-targeted maximum considered earthquake (MCE) level. Hysteresis response, damage severity and pattern index estimation, energy dissipation ability and damping ratio, relative self-centering efficiency (RSE), residual drift, peak roof drift and inter-story drift ratio, floors acceleration, shear force ratio, column curvature ductility, prestressing and gap opening effect of all buildings were studied. The results of the parametric analysis showed by increasing the prestressing ratio, wall damage severity, residual drift and energy dissipation capacity were decreased and structural seismic demand and RSE increased, while increasing mild bars was the most efficient method for improving the energy dissipation ability. [ABSTRACT FROM AUTHOR]

A comprehensive overview of fracture propagation in concrete, covering various aspects ranging from fundamentals to applications and future directions, is presented. The introduction presents an overview of fracture propagation in concrete, emphasising its importance in understanding the behaviour of concrete structures. The fundamentals of fracture propagation are then explored, including concrete as a composite material, crack initiation and propagation mechanisms, types of fractures and the factors that influence fracture propagation. Next, experimental techniques for studying fracture propagation are discussed, encompassing both destructive and non-destructive testing methods, such as acoustic emission, ultrasonic testing, digital image correlation and advanced imaging techniques like X-ray computed tomography and scanning electron microscopy. Modelling approaches, including continuum damage mechanics, the finite-element method, the discrete-element method, the lattice discrete particle model and hybrid models, for simulating and predicting fracture propagation behaviour are then reviewed. The applications of fracture propagation in concrete are highlighted, including structural health monitoring, design optimisation, failure analysis and repair and rehabilitation strategies. Research opportunities for further improvement are addressed. This article should serve as a valuable resource for researchers, engineers and professionals in the field, providing a comprehensive understanding of fracture propagation in concrete and guiding future research endeavours. [ABSTRACT FROM AUTHOR]

The crack resistance of concrete structures with low reinforcement ratios requires a broader examination. It is particularly important in the case of foundations working in changing subsoil conditions. Unfavorable phenomena occurring in the subsoil (e.g., ground subsidence, landslips, non-uniform settlement) can lead to unexpected cracking. Therefore, it is necessary to check the effectiveness of the low reinforcement provided. As there are limited studies on lightly reinforced concrete structures, we performed our own experimental investigation and numerical calculations. In the beams analyzed, the reinforcement ratio varied from 0.05% to 0.20%. It was found that crack resistance in concrete members depends on the reinforcement ratio and steel bar distribution. A comprehensive method was proposed for estimating the crack resistance of lightly reinforced concrete members in which both the reinforcement ratio and the reinforcement dispersion ratio were taken into account. Furthermore, the method considered the size effect and the fracture properties of concrete. The proposed method provides the basis for extrapolation of the test results obtained for small elements and conclusions for members with large cross-sections, such as foundations, which frequently use lightly reinforced concrete. [ABSTRACT FROM AUTHOR]

AbstractThe integration of advanced nanocomposites into concrete structures presents a promising avenue for enhancing their mechanical properties and durability. However, accurately predicting the vibrations of such structures remains a complex and challenging task due to the nonlinear behavior of the materials involved and the intricate interplay of various influencing factors. In this study, we propose the application of machine learning techniques as a powerful tool for estimating the vibrations of concrete structures reinforced with advanced nanocomposites. By leveraging the vast amounts of data generated from mathematical modeling, machine learning algorithms can effectively capture the intricate relationships between material properties, structural configurations, environmental conditions, and vibration responses. This article provides mathematical modeling simulation as the input of machine learning methods suitable for vibration prediction in concrete structures, including artificial neural networks. Furthermore, we discuss the key considerations and challenges associated with developing accurate and reliable machine learning models for this specific application domain, such as feature selection, model complexity, data quality, and interpretability. Through a comprehensive mathematical modeling, we highlight the potential of machine learning as a versatile and efficient tool for predicting vibrations in concrete structures reinforced with graphene oxide powders (GOPs). The integration of machine learning into the design, analysis, and maintenance of such structures not only facilitates more accurate predictions of vibration behavior but also enables proactive decision-making and optimization of structural performance. Finally, we outline future research directions and opportunities for further advancing the application of machine learning in the field of structural engineering and nanocomposite materials. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the effectiveness of machine learning (ML) models in predicting concrete damage using electromechanical impedance (EMI) data. From numerous experimental evidence, the damaged mortar sample with surface-mounted piezoelectric (PZT) material connected to the EMI response was assessed. This work involved the different ML models to identify the accurate model for concrete damage detection using EMI data. Each model was evaluated with evaluation metrics with the prediction/true class and each class was classified into three levels for testing and trained data. Experimental findings indicate that as damage to the structure increases, the responsiveness of PZT decreases. Therefore, we examined the ability of ML models trained on existing experimental data to predict concrete damage using the EMI data. The current work successfully identified the approximately close ML models for predicting damage detection in mortar samples. The proposed ML models not only streamline the identification of key input parameters with models but also offer cost-saving benefits by reducing the need for multiple trials in experiments. Lastly, the results demonstrate the capability of the model to produce precise predictions. [ABSTRACT FROM AUTHOR]

The post-fire assessment of concrete structures is a complex task that requires the integration of multiple measurements from different techniques. The current approach to integrate information from different sources relies mainly on expert judgement, meaning that no explicit consideration is given to the precision of different techniques. This paper presents a Bayesian updating methodology that integrates information from different sources about the maximum temperature the concrete experienced during fire exposure at a certain depth, such as discoloration and rebound hammer measurements, by considering the uncertainties and errors associated with measurements. The data is then used to update the prior information on the uncertain parameters of interest, here the fire load density and opening factor. These updated distributions provide a better estimate of the fire exposure, thermal and damage gradient and the residual condition of the structure. The proof-of-concept and effectiveness of the proposed methodology are demonstrated through a case study. The results show that the proposed methodology is able to effectively incorporate the uncertainties and errors associated with the assessment techniques, producing more reliable estimates of the fire severity. This method has the potential to improve the post-fire assessment process and provide more accurate information for the rehabilitation of concrete structures. [ABSTRACT FROM AUTHOR]

The article discusses the use of Structural Health Monitoring (SHM) techniques to ensure the safety and longevity of civil infrastructure. It focuses on damage identification methodologies and the use of numerical models to simulate the behavior of concrete structures under different fracture conditions. The article emphasizes the importance of accurate numerical strategies and comprehensive analysis of existing structures. It also presents the results of studies on the degradation of natural vibration frequencies and the detection of damage in plain concrete structures. Additionally, the article provides a compilation of research articles covering various topics related to vibration-based damage detection and structural health monitoring. Lastly, it includes a list of references cited in a research paper on the degradation of vibration characteristics in reinforced concrete beams and micro-fracture instabilities in granular solids. [Extracted from the article]

Although research has shown a considerable influence of the pre-cracking phenomenon on steel-congested concrete members, only normal concrete (NC) has been considered in the literature. The intention in this paper is thus to study the effect of the pre-cracking phenomenon on the bond response of pre-cracked NC with different slump flow values and self-consolidating concrete (SCC). Initial crack widths ranging from 0.0 to 0.5 mm are studied. Results show that initial crack widths larger than 0.10 mm have a significant influence on bond properties, such that reduction factors greater than 30% and 50% are obtained for the maximum bond strength of concrete specimens exposed to initial crack widths of 0.2 mm and 0.4 mm, respectively. Results show that concrete mixtures with higher workability are less sensitive to the pre-cracking phenomenon as compared to NC mixtures. The average bond stress of steel rebar in the pre-cracked SCC is found to be similar to that of the NC with a slump flow of 200 mm, which is considerably better than for NC with a slump flow of 97 mm. Moreover, results show that 65.8, 80.6, 88.5 and 93.1% fracture energy reductions are obtained for crack widths of 0.20, 0.30, 0.40 and 0.50 mm, respectively, as compared to the small crack width of 0.15 mm. [ABSTRACT FROM AUTHOR]

The ultrasonic echo technique is widely used in non-destructive testing (NDT) of concrete objects for thickness measurements, geometry determinations and localization of built-in components. To improve ultrasonic imaging of complex concrete structures, we transferred a seismic imaging technique, the Reverse Time Migration (RTM), to NDT in civil engineering. RTM, in contrast to the conventionally used synthetic aperture focusing technique (SAFT) algorithms, considers all wavefield types and thus, can handle complex wave propagations in any direction with no limit on velocity variations and reflector dip. In this paper, we focused on the development, application and evaluation of a two-dimensional elastic RTM algorithm considering horizontally polarized shear (SH) waves only. We applied the elastic SH RTM routine to synthetic ultrasonic echo SH-wave data generated with a concrete model incorporating several steps and circular cavities. As these features can often be found in real-world NDT use cases, their imaging is extremely important. By using elastic SH RTM, we were able to clearly reproduce almost all reflectors inside the concrete model including the vertical step edges and the cross sections of the cavities. We were also capable to show that more features could be mapped compared to SAFT, and that imaging of complex reflectors could be sharpened compared to elastic P-SV (compressional-vertically polarized shear) RTM. Our promising results illustrate that elastic SH RTM has the potential to significantly enhance the reconstruction of challenging concrete structures, representing an important step forward for precise, high-quality ultrasonic NDT in civil engineering. [ABSTRACT FROM AUTHOR]

It is common practice in earthquake-prone areas to design structures with earthquake loading in mind. The severity of the damage to a structure depends on the earthquake's magnitude and the type of structure. Sometimes, a high-intensity earthquake can trigger explosive materials to explode inside structures. This combination of loading scenarios could lead to an overall increase in damage to the structures. So, it is essential to study the vulnerability analysis of the structures subjected to multi-hazard loading scenarios. This study mainly discusses the combined effect of the earthquake followed by the blast loading on the reinforced concrete structure. The results show that damage to the structure increases under combined earthquake and blast loading. Furthermore, the earthquake-induced vibrations in the structure intensify after the blast load. A parametric study of the structure is also performed, taking into account the possible load combinations of earthquake and blast loading. Moreover, this study discusses a numerical solution to the aforementioned problem, which strengthens fundamental knowledge and provides structural engineers with a deeper insight regarding the multi-hazard loading scenario considered in this study. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslide, a cascading hazard in hilly regions, can substantially increase the damaging effects of an earthquake. This necessitates performance evaluation of buildings situated in hilly regions under the effects of landslide-only and landslides following earthquakes. In this study, the performance of two commonly observed hillside building configurations is assessed—namely, step-back and split foundation—subjected to the above-mentioned multi-hazard scenarios using an uncoupled approach. The uncertainty in the soil properties that influence the landslide-induced forces is considered based on laboratory tests of soil samples collected from active landslide sites and an existing database of landslide events. Three-dimensional hillside buildings are analysed for uphill-side landslide scenarios by performing force-controlled non-linear static analyses. Further, a suite of earthquake ground motions is scaled to a predefined intensity level, and then non-linear time history analyses are performed to obtain the damaged state of the buildings prior to assessing the landslide response. It is observed that the earthquake loading history and its direction of excitation significantly affect the response of hillside buildings to a landslide following an earthquake. Further, the step-back building is observed to be more vulnerable to landslide damage when compared to its split foundation counterpart for a given uphill-side landslide scenario. [ABSTRACT FROM AUTHOR]

The post-fire performance of slag-based geopolymer materials in structures under cyclic loading has yet to be reported and the condition of the rebars inside such structures remains unexplored. In the present work, cyclic tests were conducted on exterior beam–column joints made with a slag-based geopolymer after exposing the specimens to natural fire. The mechanical properties of the reinforcement embedded in fire-exposed specimens were also determined and analysed. The cyclic test results indicated that deterioration of the geopolymer and rebar altered the failure pattern of the fire-exposed specimen from the desired beam-end hinging to joint shear failure. The peak load, stiffness, ductility and energy dissipation reduced significantly in the fire-exposed specimens compared with the unheated control specimens. [ABSTRACT FROM AUTHOR]

This work involved investigating formwork surface characteristics and concrete surface quality. The results show that release agent types and formwork materials have a great impact on the wettability of a formwork–concrete interface, although all the formwork surfaces, with or without release agents, are hydrophilic. Remarkably, inhomogeneous roughness characteristics are only observed for a steel formwork. Plywood and steel formworks have almost the same roughnesses; however, the roughness profile of plywood is sharper than that of steel. Mineral oil always correlates with larger surface voids and higher surface void ratios (SVRs). Steel formworks significantly increase SVR, compared with plywood and plastics formworks. When SVR < 1‰ and the proportion of small voids (<500 μm) > 50%, the concrete surface appears to be bughole-free. Rusty stains left by a steel formwork aggravate concrete discoloration, especially when water-soluble release agents are applied. To improve concrete surface quality, plywood formwork is more favourable, although release agents and vegetable-oil-based emulsion are more suitable for plastics formworks. The surface area roughness of concretes is mainly determined by that of formworks. The typical microgroove feature on a formwork surface leaves no trace on the formed concrete surface; this might be owing to the difference in dimension between microgrooves and cement particles and the presence of release agents. [ABSTRACT FROM AUTHOR]

The construction industry is responsible for nearly half of the UK's carbon dioxide emissions and the use of an extremely large volume of concrete – the world's most widely used man-made material – accounts for more than 7% of global carbon dioxide emissions. The scale of this problem spawned research to explore the potential for structurally efficient non-prismatic geometries to reduce the amount of concrete used in building elements substantially, thus also reducing their embodied carbon dioxide footprint. In particular, the research focused on segmented thin concrete shells as floor slabs, leveraging computational design and digital fabrication methodologies to automate their production off site. An important part of this research was the development of a computational framework for the design of thin concrete shells in order to make such a construction methodology accessible to building designers in practice. The framework combines solutions for parametric modelling, finite-element analysis, isogeometric analysis, form-finding and optimisation, along with embedded fabrication constraints specific to the project's automated manufacturing system. The application of the developed computational framework in the design of a 4.5 m × 4.5 m prototype is documented in this paper, illustrating how automating concrete construction can transform the industry towards net-zero. [ABSTRACT FROM AUTHOR]

The Port Authority of New York and New Jersey's Bayonne Bridge crosses the entrance to the ports of Newark and Elizabeth, NJ, USA. The longest steel truss arch span in the world when it opened in 1931, the bridge was designed by Othmar Ammann. To maintain the economic vitality of the ports, the original 46 m navigational clearance needed to be raised to 65.5 m in order to accommodate mega container ships passing through the newly widened Panama Canal. Precast concrete segmental construction was used along with an innovative staged construction approach to avoid long-term bridge closures and to expedite the construction schedule. The new navigational clearance was attained in 2017 and project completion occurred in mid-2019. [ABSTRACT FROM AUTHOR]

The Gordie Howe International Bridge project is providing a new modern border crossing between Windsor, ON, Canada and Detroit, MI, USA. The centrepiece of the project is a 2.5 km long cable-stayed bridge with 853 m main span over the Detroit River and 220 m tall towers on both sides of the river. In addition to the bridge, the project includes ports of entry on both sides of the border and a new freeway interchange with Interstate-75 in Detroit. The project was procured as a public–private partnership that allowed the proponents to propose either a suspension or cable-stayed bridge, with the ultimate selection creating the longest cable-stayed bridge in North America. The paper outlines the need for the new border crossing and results of the environmental assessment process, as well as describing the project evolution from planning through procurement and into detailed design. The scoping of the bridge design parameters is discussed, including: comprehensive geotechnical investigation; load study to develop project-specific live loading; the approach for durability requirements to achieve a service life of 125 years; special considerations for security; wind engineering; and compliance with both Canadian and US design codes. The paper also discusses the approach to achieving an aesthetically pleasing bridge design. [ABSTRACT FROM AUTHOR]

Deep penetrating sealer (DPS) is a general term for a class of waterproofing agent that can fill the internal pores of concrete; it is produced by mixing an alkali metal silicate solution as the base material with catalysts and additives. The objective of this study was to investigate the characteristics and mechanism of the effect of different amounts of DPS sprayed on concrete. The water absorption, hydrophilicity, permeability, microhardness, abrasion erosion resistance and pore structure of concrete were tested at different water/cement ratios and amounts of sprayed DPS. The test and analysis results showed that, compared with a control concrete, concrete absorbed less water and exhibited less permeability after being sprayed with DPS. Moreover, the hydrophilicity, microhardness and abrasion erosion resistance improved, while the pore volume significantly decreased. The effect was more evident when the amount of sprayed DPS was higher. This study provides a reference for practical engineering applications by demonstrating the variation in concrete surface properties after spraying different amounts of DPS. [ABSTRACT FROM AUTHOR]

Reductions in materials consumption through the use of high-strength materials is important in building construction. However, high-strength steel applications have possible challenges such as their effects on bond behaviour and structural ductility. The bond behaviour, total energy capacity and failure modes of headed and straight high-strength and normal-strength steel bars were investigated using pull-out tests. In the experimental programme, 48 concrete specimens with a compressive strength of 40 MPa, different ratios of steel fibres and embedded steel bars with different yield strengths were subject to pull-out tests. The results showed that the headed bars had far greater bond stress and total energy capacity than the straight bars. It was also found that the higher the yield strength of the bars, the higher the energy capacity and bond stress. Although increases in fibre ratios led to increases in energy capacity, they did not absolutely increase the bond stress. The specimens with both headed and straight bars failed by splitting. [ABSTRACT FROM AUTHOR]

Concrete half-joint beams (HJB) are characterised by local reductions in the overall beam depth at the support and are ideal for structural connections. However, the beam joint region is vulnerable to deterioration and design/detailing shortcomings. Non-linear numerical modelling of the behaviour of a series of impaired reinforced concrete HJBs was conducted in this study. The major defects considered were unavailability of diagonal bars/U bars, shear reinforcement inadequacy and reinforcement corrosion. The numerical simulations captured global and local behaviours of the beams. The load capacity prediction discrepancy was in an impressive range of −16.7% to +1.5%, in contrast to the predictions of the commonly used strut-and-tie model in the range of −39.2% to 16.9%. Subsequently, an experimental study was established using finite-element modelling to explore the retrofitting potential of defective HJBs using the deep embedment (DE) technique. The strength enhancement was almost 30% and the internal load paths and the failure mode of the defective beam were favourably altered with the post-installation of the retrofitting element. The DE technique was thus identified as an ideal option for the retrofitting of impaired HJBs. [ABSTRACT FROM AUTHOR]

An investigation was carried out into the structural performance of concrete columns reinforced with various shapes of glass-fibre-reinforced polymer bars and stainless steel stirrups under concentric loading at ultimate limit state. Six square-section columns were cast to investigate the effects of different reinforcement types. The results showed failure modes depended on reinforcement material, shape and stirrup spacing. Across all specimens, steel-reinforced columns had higher loading capacity and better ductile performance, followed by L-shape and then round polymer bars. Smaller spiral spacing increased confinement efficiency and ductility and provided sufficient restraint against longitudinal polymer bar buckling. Finite-element models were also calibrated, and the results were in close agreement with experimental measurements. Based on the calibrated models, numerical parameters were studied to understand further the behaviour of composite columns reinforced with glass-fibre-reinforced polymer. [ABSTRACT FROM AUTHOR]

Modern infrastructure heavily relies on robust concrete structures, underscoring the critical need for effective monitoring to ensure their safety and durability. This paper addresses this imperative issue by introducing an innovative automated and wireless system for continuous structural monitoring. By employing embedded electrical resistivity sensors coupled with a wireless-based data transmission mechanism, real-time data collection becomes feasible. We provide a general description of the system's architecture and its application in a pilot study covering the effects of the devices on concrete properties and data transmission. The dielectric properties of concrete specimens were investigated under natural and accelerated curing/degradation and the results were used in the final design of the antenna device. Furthermore, a pilot test comprising four reinforced concrete columns was used to investigate the range of data transmission from inside to outside of the concrete, the effects of the hardware device on the compressive strength and concrete distribution in the columns, and the data transmission quality in real time under realistic exposure conditions. [ABSTRACT FROM AUTHOR]

In the framework of the international benchmark "Vulcain tests on 3 Walls", three full scale ISO fire tests on a reinforced concrete wall, simultaneously subjected to a constant uniaxial compressive load, were conducted in CSTB in Paris, France. The duration of these tests exceeded 120 min. In order to assess the capability of available finite elements (FE) models to represent reinforced concrete structural behaviour under fire exposure, IRSN, within the scope of the Vulcain benchmark, set up a modelling with help of Mazars isotropic continuum damage mechanical model (Mazars Application de la mécanique de l'endommagement au comportement non linéaire et à la rupture du béton de structure. PhD Thesis, Univ. Paris 6, ENS Cachan, France, 1984) ran with the CAST3M software (Verpeaux et al. in Fouet, Ladevèze and Ohayon (eds), Calcul des Structures et Intelligence Artificielle, Pluralis, 1988), while a user-defined procedure was used to calculate the concrete properties taking into account load induced thermal strains (LITS). The effect of LITS according to the model of Anderberg and Thelandersson (Stress and deformation characteristics of concrete at high temperatures: 2 experimental investigation and material behavior model, Bulletin 54, Lund Institute of Technology, Lund, 1976) was considered by an explicit term in the strain decomposition. Also, the numerical simulation of three full scale ISO fire tests of Vulcain was carried out with the help of a FE model that takes into account the values of the concrete thermomechanical parameters identified. The numerical model is composed of two different parts, one for heat transfer analysis and the other one for structural analysis based on a three-dimensional FE model including concrete and reinforcing steel meshes. It is illustrated through this numerical investigation that the proposed model can predict the thermomechanical behaviour of reinforced concrete walls under fire exposure. [ABSTRACT FROM AUTHOR]

The influence of shape memory alloy (SMA) reinforcement on the non-linear static and dynamic responses of hollow-section concrete columns to earthquakes was investigated. The seismic performance of concrete columns reinforced with steel and with a mix of SMA and steel was compared by assessing their ductility ratios, dissipated energies, strain recovery capacities and collapse drifts. The influence of near- and far-field ground motions on the static and dynamic responses of the test columns was also investigated. To verify the results, the columns were subjected to a near-field ground motion related to an existing earthquake and the relative displacement response history was compared with experimental results. Compared with the steel-reinforced column, it was found that the use of the hybrid SMA–steel reinforcement was more effective at recovering strains and reduced the residual deflections. On the other hand, the steel-reinforced column had a greater ductility ratio and energy dissipation capacity. It is therefore important to find an optimal combination length for the two types of reinforcement in a concrete column. The results indicated that the optimum reinforcement configuration involves replacing steel with SMA at 15–20% of column height, which includes the probable plastic hinge length. [ABSTRACT FROM AUTHOR]

It is frequently reported that groundwater containing salt flowing into underground reinforced concrete structures results in steel reinforcement corrosion. However, little has been done to study the impact of such chloride-induced deterioration on structural performance when an external load acts on the underground structure, such as ground subsidence or the movement related to faults. In this study, structural experiments and finite-element analysis of the interaction between an underground structure and the ground were undertaken to clarify the failure modes and verify the numerical analysis. The failure behaviour of an underground structure with corroded steel subject to ground displacements was then examined analytically. It is concluded from the study that chloride-induced deterioration can impair the durability of an underground structure, making it difficult to maintain the inner space, and the failure mode is related to the location of the steel corrosion, which can change the direction of the shear zone in the peripheral ground. [ABSTRACT FROM AUTHOR]

Split Hopkinson pressure bar (SHPB) is commonly used to characterise materials under high strain rates. However, conventional SHPB tests on brittle materials have encountered several experimental challenges for high strain rate loading. In relatively brittle materials like concrete, the deformation of the specimen is very small when subjected to impact loading; hence, it is very difficult to obtain the prerequisites of valid SHPB tests such as dynamic equilibrium and constant strain rate in the specimen. To overcome these issues, the current study presents the importance of the pulse shaper approach in SHPB application for the dynamic characterisation of concrete material. Selection of the appropriate dimension of pulse shaper assists in facilitating dynamic stress equilibrium and constant strain rate in the specimen. In the present study, copper pulse shapers are used for the evaluation of concrete under high strain rate loading using an SHPB set-up. Parameters such as the effect of dimensions (diameter and thickness) of pulse shapers on loading pulses, dynamic equilibrium, constant strain rate and material responses are studied. Experimental results reveal the prediction of suitable pulse shapers for 50–200/s strain rates. In addition, numerical simulation is also performed and results are validated with the experimental data. [ABSTRACT FROM AUTHOR]

The ultrasonic echo technique is frequently used in non-destructive testing (NDT) of concrete structures for thickness measurements, geometry determinations as well as localization of built-in components. To improve ultrasonic imaging of complex structures in concrete, we transferred a geophysical imaging technique, the reverse time migration (RTM), to NDT in civil engineering. In contrast to the conventionally used synthetic aperture focusing technique (SAFT) algorithms, RTM is a wavefield continuation method in time and uses the full wave equation. Thus, RTM can handle complicated wave propagations in any direction without dip limitation. In this paper, we focused on the application and evaluation of a two-dimensional (2D) elastic RTM algorithm considering compressional waves, vertically polarized shear waves, and Rayleigh waves. We tested the elastic RTM routine on synthetic ultrasonic echo data generated with a 2D concrete model consisting of several steps and circular air inclusions. As these complex structures can often be found in real-world NDT use cases, their imaging is especially important. By using elastic RTM, we were able to clearly reproduce vertical reflectors and lower edges of circular air voids inside our numerical concrete model. Such structures cannot be imaged with conventional SAFT algorithms. Furthermore, the used elastic RTM approach also yielded a better reconstruction of a horizontal reflector and upper boundaries of circular air inclusions. Our encouraging results demonstrate that elastic RTM has the potential to significantly improve the imaging of complex concrete structures and, thus, is a step forward for detailed, high-quality ultrasonic NDT in civil engineering. [ABSTRACT FROM AUTHOR]

Reinforced concrete bridge size box culverts (RCBCs), with structure length greater than or equal to 6.1 m (20 ft), are an integral part of the transportation infrastructure. Load rating RCBCs is essential, and the critical component is generally the top slab. The objective of this study is to propose a triage methodology to quickly and conservatively determine the rating factor (RF) for the top slab of a two-cell RCBC. The derivation of the demand equations is illustrated in this manuscript for the top slab and the AASHTO HL-93 live load. The finite element program CANDE is used to evaluate the influence of the cell clear height (H), cell clear span (S), fill height (HF), and the backfill modulus (E) on the demand loads. Multiple regression analyses showed that H had no significant effect on the dead load demands, while H and E had no significant effect to the live load demands. Conservative linear demand equations (LDEs) are presented to calculate the dead and live load demands leading to a quick determination of the RF. LDEs for single and multi-cell culverts can be derived following the LDE procedure outlined herein. [ABSTRACT FROM AUTHOR]

This study investigated the distress conditions of test sections of concrete pavement that have been used for more than the design life of 20 years. The study was performed using data from a highway pavement management system. To determine the effects of distress and environmental factors, weather data were analysed for the test sections. Regression models were developed for five types of distress: longitudinal cracks, asphalt patching, concrete patching, durability cracks and longitudinal spalling. The influence of environmental factors was analysed, such as the days with temperature below zero, number of freeze and thaw cycles, average relative humidity and the amount of de-icing salt consumed. Using these models, the amount of distress was predicted and compared with the actual amount of distress. The predicted amount of distress was similar to the actual amount of distress, indicating that the models were properly developed. Using the results of this study, the distress of aging concrete pavement could be reduced by managing the environmental factors more appropriately. [ABSTRACT FROM AUTHOR]

The seismic design of new structures in Iran is currently based on earthquake hazard maps for a fixed return period of 475 years. However, this can lead to an unequal level of risk due to uncertainty in collapse capacity and differences in hazard curves in different locations. In the work reported in this paper, a risk-targeted approach was used to determine the peak ground acceleration (PGA) for designing reinforced concrete moment-resisting-frame (RC MRF) buildings in four important port cities in southern Iran. The three main parameters considered for determining risk-targeted PGA were the structural fragility curve, the seismic hazard curve and the target annual collapse probability. Nine RC MRF buildings with different geometrical characteristics were evaluated and their fragility curves were extracted. Then, using a risk integral, the annual collapse probability and PGA of the frames were obtained for the studied cities. Comparison of the results showed that an approach proposed in the literature for Europe is in accordance with the results of the approach proposed in this paper. [ABSTRACT FROM AUTHOR]

The study was conducted to find the effect of using brick as a coarse aggregate in reinforced concrete (RC) beams and to obtain optimum replacement ratios without significantly reducing the durability (i.e. corrosion probability in reinforcement and chloride ion resistivity in reinforcement cover) and flexural strength of the specimen subjected to a marine environment. Specimens were exposed to alternate wetting and drying cycles in an artificially prepared corrosive environment (chloride ions with a concentration of 50 parts per 1000). A regression model for finding the overall mass loss in the reinforcement after the effect of corrosion is presented. A regression model to obtain the different fundamental properties of the concrete through replacing stone by brick aggregate is also presented. This regression model was validated using the k-fold validation approach. [ABSTRACT FROM AUTHOR]

The need for reliable test data to verify scientific theories together with the necessity to calibrate code‐oriented expressions and their level of safety have pushed researchers to develop test databases. Such databases collect the work of multiple independent scientific initiatives compiling experimental evidences on similar test configurations but with different parameters such as geometry, size of the component tested, boundary conditions, and/or mechanical properties of the materials. Such databases, usually collected for specific purposes, have traditionally faced maintenance, and especially consistency issues. Also, they have given rise to conflicting values due to different interpretations of the same data entries in databases elaborated by different researchers. In addition, such databases are frequently designed and configured to serve for one purpose, although their data could be used in multiple manners. Recent developments regarding machine learning and artificial intelligence have opened the door to exploiting historically collected test results by data mining techniques. In an effort to improve the current situation concerning databases, the fib has launched a wide and open initiative on test data management. It consists of a common data structure, sufficiently flexible to serve for multiple purposes, hosting very different test setups, and meant to be used for cross analyses of data. This allows reusing data originally developed for one purpose to analyze other aspects, enhancing the value of current experimental work. Also, a consistent management structure is defined taking advantage of the fib network, with database editors, collectors and users, allowing for a transparent and documented peer‐review process while incorporating new test data. This paper presents the main basis grounding the test data management as well as details on the first two completed applications, referring to fiber‐reinforced concrete (material data) and to punching of slab‐column connections (structural response), showcasing the framework versatility and universality. [ABSTRACT FROM AUTHOR]