Search

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]

This work presents the application of a finite element model utilizing a three-dimensional (3D) probabilistic semi-explicit cracking model to analyze the rupture process of a large concrete wall beam. The numerical analysis predicts both the global behavior of the structure and its primary rupture mechanisms, utilizing three different finite element mesh refinements to ensure robustness. A Monte Carlo (MC) procedure is integrated into the modeling approach to account for probabilistic variations of the material properties. The statistical analysis derived from this probabilistic model may sometimes result in overly conservative safety coefficients, particularly when using a coarse mesh. Additionally, the detailed understanding of the structure's cracking process, regardless of its rupture mechanism, may experience some reduction in precision. Due to the necessity of numerous simulations to achieve statistically significant results, the MC procedure can become computationally expensive. To address this, a straightforward parallelization of the Monte Carlo procedure was implemented, allowing multiple finite element analyses to be conducted concurrently. This strategy significantly reduced computational time, thereby enhancing the efficiency of the numerical model in performing numerical simulations of structural engineering. [ABSTRACT FROM AUTHOR]

Recent earthquakes have demonstrated that code-conforming modern (i.e. post-1970s) reinforced concrete (RC) buildings can satisfy life safety performance objectives. However, the accumulated earthquake damage in these modern buildings raised concerns about their performance in future events, contributing to widespread demolition and long-term closure of damaged buildings. The economic and environmental impacts associated with the demolition and long-term closure of modern buildings led to societal demands for improved design procedures to limit damage and shorten recovery time after earthquakes. To address societal demands, this study proposes a damage-control-oriented seismic design approach that targets functional recovery by ensuring structural component demands do not exceed the damage-control limit state (DLS) under design-level events. Herein, DLS is defined as the post-earthquake state beyond which the strength and deformation capacity of a structural component is compromised, and its performance in a future event cannot be relied upon without safety-critical repair. This study proposes a methodology to determine component deformation limits for the design of structures for damage control. Using the developed methodology, we propose component rotation limits for RC beams, columns, and walls. The seismic performance and capability of buildings designed using the proposed design approach to satisfy recovery-based performance objectives is demonstrated through nonlinear response history and recovery analyses (using the ATC-138 methodology) of four archetype frame buildings, designed per New Zealand standards to different beam deformation limits. The analyses show that building codes can achieve functional recovery using the proposed component deformation limits without the need for sophisticated recovery analyses. [ABSTRACT FROM AUTHOR]

Concrete structures undergo integral fragmentation under explosion loads. The fragmentation degree and particle-size distribution of concrete blocks under explosion loads must be considered during mining to ensure safety. In this study, the impulse is calculated based on the relationship between overpressure and time, and the impact energy of the explosion wave is obtained based on blast theory. Subsequently, the Mohr-Coulomb shear strength fracture criterion is introduced to determine the ultimate shear stress of the concrete materials, and an empirical model that can effectively calculate the energy consumption of concrete blocks under explosion loads is established. Furthermore, concrete fragments with different particle sizes under explosion scenarios are quantitatively predicted with the principle of energy conservation. Finally, explosion tests with different top standoff distances are conducted, and the concrete fragments after the explosion tests are recovered, sieved, weighed, and counted to obtain experimental data. The effectiveness of the fragment empirical model is verified by comparing the model calculation results with the experimental data. The proposed model can be used as a reference for civil blasting, protective engineering design, and explosion-damage assessment. [ABSTRACT FROM AUTHOR]

Concrete structures are commonly used as secure settlements and strategic shelters due to their inherent strength, durability, and wide availability. Examining the robustness and integrity of strategic concrete structures in the face of superenergy projectiles is of utmost significance in safeguarding vital infrastructure sectors, ensuring the well-being of individuals, and advancing the course of worldwide sustainable progress. This research focuses on forecasting the penetration depth (BPD) through the application of robust models, such as Multilayer Perceptron (MLP), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbors (KNN) as ML models. The dataset used consists of 1,020 data points sourced from the National Institute of Standards and Technology (NIST), encompassing various parameters such as cement content (Cp), ground granulated blast-furnace slag (GGBFS), fly ash content (FA), water portion (Wp), superplasticizer content (Sp), coarse aggregate content (CA), fine aggregate content (FAA), concrete sample age (t), concrete compressive strength (CCS), gun type (G-type), bullet caliber (BCali), bullet weight (Wb), and bullet velocity (Vb). Feature selection techniques revealed that the MLP model, incorporating eight input variables (FA, CA, Sp, GGBFS, Cp, t, FAA, and CCS), provides the most accurate predictions for BPD across the entire dataset. Comparing the four models used in this study, KNN demonstrates distinct superiority over the other methods. KNN, a non-parametric ML model used for classification and regression, possesses several advantages, including simplicity, non-parametric nature, no training requirements, robustness to noisy data, suitability for large datasets, and interpretability. The results reveal that KNN outperforms the other models presented in this paper, exhibiting an R² value of 0.9905 and an RMSE value of 0.1811 cm, signifying higher accuracy in its predictions compared to the other models. Finally, based on the error analysis across iterations, it is evident that the final accuracy error of the KNN model surpasses that of the SVM, MLP, and LightGBM models, respectively. [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]

The use of synthetic materials with high corrosion resistance in a concrete matrix yields structures that are more durable and suitable for use in aggressive environments, eliminating the need for frequent maintenance. Examples of such materials include glass (GFRP) and basalt (BFRP) fiber-reinforced polymer bars (FRP). Due to the low modulus of elasticity of these bars, concrete elements reinforced with FRP longitudinal rebars tend to exhibit cracks with wider openings and greater depths compared to those reinforced with steel rebars, which diminishes the element's shear resistance. The addition of discontinuous fibers into the concrete aims to maintain stress transfer across the cracks, thereby enhancing the shear capacity and ductility of FRP-reinforced structures. This study evaluates the impact of fiber addition on the shear resistance of concrete beams reinforced with FRP rebars. An experimental investigation was conducted, focusing on the partial and complete substitution of stirrups with polypropylene macro fibers in concrete beams reinforced with FRP longitudinal rebars and stirrups. This research examined beams reinforced with glass (GFRP) and basalt (BFRP) fiber-reinforced polymer bars. For the initial set of beams, all stirrups were replaced with synthetic macro fibers. In the subsequent set, macro fibers were added to beams with insufficient stirrups. Although the complete replacement of GFRP and BFRP stirrups with polypropylene macro fibers did not alter the brittle shear failure mode, it did enhance the shear resistance capacity by 78.5% for GFRP-reinforced beams and 60.4% for BFRP-reinforced beams. Furthermore, the addition of macro fibers to beams with insufficient stirrups, characterized by excessive spacing, changed the failure mode from brittle shear to pseudo-ductile flexural failure due to concrete crushing. In such instances, the failure load increased by 18.8% for beams with GFRP bars and 22.8% for beams with BFRP bars. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study conducted on the behavior of drop-in anchors in uncracked concrete slabs. Both seismic (cyclic) load tests and static load tests to collapse are performed on drop-in anchors subjected to tension or shear forces. Three different anchor sizes are subjected to seismic qualification testing, followed by a static load test to collapse. The test results confirm the capability of the tested anchors to sustain simulated pulsating seismic tension and shear loading with frequency ranges between 0.1 and 2.0 Hz. It was observed that no tension failure occurred at the end of the cyclic load tests for all the tested anchors, and their residual inelastic maximum displacement at the end of the cyclic tension test was relatively small. Moreover, the experimental results show that the anchors' ultimate capacities are higher than those specified by the anchor manufacturer. Finally, the anchors' experimental pullout shear capacities are compared with the failure prediction equations in the literature and design codes. It is found that the theoretical models provide a conservative prediction of the concrete breakout of anchors in tension compared to the experimental ultimate loads. The coefficient for pry-out strength (kcp) equal to 2 or slightly smaller than 2 is likely to predict a better pry-out capacity with the experimental results compared to the application of the high conservative value of kcp equal to 1, as given in the code. [ABSTRACT FROM AUTHOR]

In the past five years, the implementation of machine learning (ML) techniques has surged in civil engineering applications, particularly for optimizing and predicting solutions to various challenges. More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. These models may be used to estimate the compressive strength of masonry or repair mortars, probable damage scenarios in buildings, concrete models, beams, and columns for determining the mechanical characteristics of materials, damage detection in civil structures, and so on. This comprehensive review aims to clarify the array of ML-based methods employed in civil engineering, specifically focusing on their efficacy in strengthening energy efficiency and cost-effectiveness. In combination with ML, the review explores corresponding soft computing methodologies such as fuzzy logic (FL) and design of experiments (DOE). A variety of case examples that highlight the versatility of these approaches, particularly in applications linked to structural reinforcement, enhance the story. The review navigates difficulties associated with the integration of soft computing in civil engineering and expands its scope to include emerging research directions. This synthesis of advanced artificial intelligence (AI) serves as a guide, providing new researchers with knowledge about a developing field. These methods could revolutionize the current situation by providing creative answers to complex problems that arise in civil structural applications.

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]

The present work investigates the crack-induced degradation of the dynamic characteristics in plain concrete elements subjected to mixed-mode fracture conditions. Specifically, a discrete fracture model, based on an inter-element cohesive approach, has been improved and implemented into a 2D finite element framework to evaluate the progressive variation of the static and dynamic properties as the damage level increases. The obtained numerical results highlight the effectiveness of the proposed model in determining the load-carrying capacity and the degradation of natural vibration frequencies during damage propagation, demonstrating its applicability in the framework of structural health monitoring

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]

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]

This study presents an innovative numerical model based on the position-based finite element method, utilizing laminated frame elements to predict beam displacements under corrosion-a common challenge in reinforced concrete structures due to chlorides and carbon dioxide penetration. The proposed model addresses both geometric and physical non-linearity, specifically considering the uniform reduction in rebar area, a critical consequence of carbonation-induced corrosion. This reduction significantly influences element inertia and, consequently, the structure's stiffness. Given the expected increase in corrosion problems in reinforced concrete structures due to climate change-induced rising temperatures and heightened carbon dioxide levels, resulting in earlier degradation and reduced stiffness, there is a pressing need for alternative approaches to calculate structure displacement under carbonation-induced corrosion. In response to this concern, our model provides an innovative and alternative methodology, demonstrating promising results when compared with existing literature, even under the constraints of a simplified model. [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]

Concrete structures are fundamental to modern infrastructure, and their efficient manufacturing is crucial for sustainable construction practices. However, traditional manufacturing processes often lack the precision and optimization required to meet evolving structural demands and sustainability goals. This deficiency becomes even more critical when considering seismic hazards, which pose a significant risk to the safety and resilience of urban infrastructure, particularly reinforced concrete buildings. Accurate assessment of seismic safety is crucial for effective risk mitigation and disaster preparedness. In this study, we introduce a novel approach that leverages a Fine-tuned Dragonfly Optimized Artificial Neural Network (FDO-ANN) to enhance the evaluation of seismic hazard safety in concrete Structures, utilizing data from the Structural Engineering Research Unit (SERU) database. Z-score normalization was employed as a data preprocessing approach to ensure the accuracy and reliability of the data utilized in the evaluation. Linear Discriminant Analysis (LDA) was used for feature extraction to identify essential characteristics or characteristics in reinforced concrete buildings that are associated with seismic safety. Python tool was used to analyze the proposed method. The proposed approach is assessed in terms of various parameters and compared to existing methods achieving an impressive accuracy of 95.6%. The proposed approach has the potential to inform more effective mitigation strategies, leading to increased resilience in the face of seismic hazards and improved protection of human lives and property.

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]

In the present study, the effect of irregularity of structures of the concrete with special shear walls has been analyzed and modeled by considering the effect of soil-structure interaction by dynamic analysis method with ABAQUS software. The results are presented in the form of diagrams with different soil interaction conditions, the effect of the number of layers was investigated for different soils. The results showed that the bearing system of the flexural frame and the concrete shear wall under the lateral load of the earthquake have good strength and ductility, which makes this issue. This structural system should be used for cities with very high earthquake risk. As the number of floors increases, the effect of the shear walls on increasing the bearing capacity decreases. As the number of floors increases, the final displacement of the frame increases. The results showed that for soil type 1(hard soil which Vs=1000 m/s, v=0.15, q=50000 Kg/m2, P=2000 Kg/m3, r=0.5 m) structural system of concrete shear walls with better displacement, maximum structural stiffness, better behavior was observed in the structure. Three-story frame with concrete shear wall has a significant increase compared to similar frames with more floors, which shows the good performance of this system in structures with fewer floors. [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]

This study presents a comparison between numerical and experimental results for reinforced concrete columns subjected to axial compression. Depending on the columns support and their organization within the structure, columns primarily work under either concentric or eccentric compression, respectively, bending in situations where horizontal actions such as wind or/and earthquakes are present in the structure. Different countries have specific design codes, and in this study, the calculation of columns is based on the European design codes, specifically EN 1992-1-1. As a common practice in most cases during research, tests are conducted using computational models, and based on the obtained results through the application of similarity theory, an attempt is made to transition to the actual behavior of structural elements. Therefore, this paper applies a logic of "almost real" testing, where two columns with square cross-sections were produced and tested. The columns had a rectangular base with cross-section dimensions of 20/20 cm and a height (L) of 300 cm, with a concrete strength of fcm,cube=61.80 MPa. They were reinforced with longitudinal reinforcement (4Ø12 mm) and had a tensile strength of ftm=588.10 MPa. Additionally, stirrups of Ø8 mm were placed at every sw=25 cm. Experimental results show a closer alignment with software calculations using SEISMOSOFT with an accuracy of 96%, while results according to EN 1992- 1-1, based on simplified methods, show 64% for the Nominal Stiffness Method and 59% for the Curvature Method. [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]

Concrete structures are fundamental to modern infrastructure, and their efficient manufacturing is crucial for sustainable construction practices. However, traditional manufacturing processes often lack the precision and optimization required to meet evolving structural demands and sustainability goals. This deficiency becomes even more critical when considering seismic hazards, which pose a significant risk to the safety and resilience of urban infrastructure, particularly reinforced concrete buildings. Accurate assessment of seismic safety is crucial for effective risk mitigation and disaster preparedness. In this study, we introduce a novel approach that leverages a Fine-tuned Dragonfly Optimized Artificial Neural Network (FDO-ANN) to enhance the evaluation of seismic hazard safety in concrete Structures, utilizing data from the Structural Engineering Research Unit (SERU) database. Z-score normalization was employed as a data preprocessing approach to ensure the accuracy and reliability of the data utilized in the evaluation. Linear Discriminant Analysis (LDA) was used for feature extraction to identify essential characteristics or characteristics in reinforced concrete buildings that are associated with seismic safety. Python tool was used to analyze the proposed method. The proposed approach is assessed in terms of various parameters and compared to existing methods achieving an impressive accuracy of 95.6%. The proposed approach has the potential to inform more effective mitigation strategies, leading to increased resilience in the face of seismic hazards and improved protection of human lives and property. [ABSTRACT FROM AUTHOR]