Search

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]

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]

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]

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.

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

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]

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]

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 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.

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]

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]

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

To investigate the effects of parameters on the seismic performance of slender T-shaped RC walls subjected to a biaxial seismic action, a numerical model was established using a fiber-based cross-section and displacement-based beam–column element. The axial load ratio, shear span ratio, flange width to web height ratio, concrete strength grade, stirrup ratio, and longitudinal reinforcement ratio were selected for the parametric study, and the effects of these parameters on the performance degradation under biaxial loading were investigated. Furthermore, a sensitivity analysis of various parameters for the decrease was conducted. The results showed that the bearing and deformation capacities under biaxial loading were both decreased, and the total energy consumption was greater than that under uniaxial loading. The impacts of different parameters and loading paths on the decrease extent were significantly different, and the overall reduction was greater in the flange direction than in the web direction. Under the square loading path, the T-shaped wall had the greatest reduction in its seismic performance, followed by the eight-shaped and cruciform loading paths. The changes in the axial load ratio, shear span ratio, and concrete strength significantly affected the performance degradation under biaxial loading. Accordingly, it is recommended to reasonably consider the values of these three parameters in a multidimensional seismic design to maintain safety redundancy. [ABSTRACT FROM AUTHOR]

The utilization of externally bonded carbon fiber-reinforced polymers (CFRPs) and glass fiber-reinforced polymers (GFRPs) for reinforcing and retrofitting components has garnered considerable interest recently, as such composites provide beneficial properties, including a high modulus of elasticity, high strength, and low weight. This work conducts a finite element analysis, verified through laboratory experiments on 14 reinforced concrete (RC) beams. The primary focus is the final load of these components, considering varying CFRP orientations relative to the loading direction. In this research, the performances of control beams and RC beams are compared to assess the effectiveness and efficiency of different strengthening methods. The results demonstrated that bonding CFRP sheets with V-shaped end anchorages on the tension side was highly effective in improving the flexural capacity of RC beams in the weaker concrete strength group. This strengthening method resulted in a substantial increase in strength (of around 29.8%) in the higher concrete strength group. In addition, utilizing V-shaped end anchorages to bond CFRP sheets on the tension side proved to be a highly efficient technique for improving flexural strength. Conversely, bonding inclined CFRP strips to the sides of RC beams was highly effective at enhancing the shear capacity of the beams. These outcomes convincingly demonstrate the effectiveness of FRP for the reinforcement of structural components. The specimens strengthened with inclined strips exhibited enhanced shear and deformation capacity compared to those strengthened with vertical strips. [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.

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.

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.

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.

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]

Based on the seismic design codes to prevent soft-story failure, columns of a soft story must be designed for amplified loads due to the discontinuity of braces or shear walls in that story. Because of the masonry infill walls discontinuity, Soft story failure has been reported in the recent earthquakes. Most national seismic design codes don't consider the effect of masonry infill walls for the design of the soft story. This paper aims to investigate the soft story failure and then present a simple formula for the design of soft-story in moment resisting frame structures. In this paper, the different arrangements of masonry infill walls are considered. Structural modeling was carried out based on reliable parameters and some national or international seismic design codes. By using nonlinear static analysis, a simple methodology is proposed and the main result is a simple formula that can be used for the engineering design of concrete moment resistant frames. [ABSTRACT FROM AUTHOR]

Ensuring the safety and stability of buildings during earthquakes is of utmost importance. This can be achieved by assessing the seismic performance of reinforced concrete structures with consideration of design details. This study focused on the seismic behavior of reinforced concrete buildings by comparing the effects of two different types of stirrups, namely those with a 135° angled end-hook shape and straight hooks, with variation of concrete strength. Pushover analysis of a sample building was performed to determine the effect of hook shape on stirrup reinforcement with a constant volumetric ratio for various concrete strength classes. The results of the analysis indicated significant differences in concrete strength and seismic behavior between the two stirrup configurations. The hooked stirrups demonstrated superior energy dissipation capability and ductility, which led to better seismic performance compared to unhooked stirrups across varying levels of concrete strength. To extend the investigation, the study compared the Mander et al., Kent–Scott–Park, and Kappos–Konstantinidis concrete models with different concrete classes (C50-C25-C20-C16-C10). The findings emphasized the importance of stirrup configuration in the design of earthquake-resistant structures. The study concluded that RC structural performance with the 135-degree hooked concrete members exhibited much better behavior of the 90-degree members for the various concrete strength. In this way, it has been revealed the arrangement and detailing of reinforcement in the construction beams and columns improves the governing effect on seismic structural performance. [ABSTRACT FROM AUTHOR]

An algebraic approach to the design of resource-efficient carbon-reinforced concrete structures is presented. Interdisciplinary research in the fields of mathematics and algebra on the one hand and civil engineering and concrete structures on the other can lead to fruitful interactions and can contribute to the development of resource-efficient and sustainable concrete structures. Textile-reinforced concrete (TRC) using non-crimp fabric carbon reinforcement enables very thin and lightweight constructions and thus requires new construction strategies and new manufacturing methods. Algebraic methods applied to topological interlocking contribute to modular, reusable, and hence resource-efficient TRC structures. A modular approach to construct new interlocking blocks by combining different Platonic and Archimedean solids is presented. In particular, the design of blocks that can be decomposed into various n-prisms is the focus of this paper. It is demonstrated that the resulting blocks are highly versatile and offer numerous possibilities for the creation of interlocking assemblies, and a rigorous proof of the interlocking property is outlined. [ABSTRACT FROM AUTHOR]

This paper describes the structural effects of ASR expansion in reinforced structures. It proposes models and procedures for calculating the strains in the concrete caused by the reinforcement, the load effects in statically indeterminate structures and the cross-sectional capacities. Three models are described to include variations of the ASR expansion in a section. Model 1 for constant or linearly varying ASR expansion, Model 2 for an expansion variation in different parts of a cross-section and Model 3 for a particular case of different expansion in subsections of the cross-section. The models are exemplified by calculations based on input data from one relevant bridge, the 70 years old Elgeseter bridge in Trondheim, Norway. This is done to illustrate various aspects of the calculation methods and show the structural consequences of the ASR expansion. The results correlate with the bridge’s observed structural behavior and assumed expansion. This paper also illustrates the importance of coordinating inspection, material testing and structural strength analysis to obtain reliable assessment.

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.

Self-compacting concrete has constructive advantages over conventional concrete, such as reducing labor and construction time, mainly because of its fluidity in the fresh state. However, in the hardened state, it maintains low performance when tensioned, and the fibers can be added to the mixture, maintaining a portion of the resistance after cracking. Steel fibers are usually added to concrete, but recently synthetic fibers have been used, due to their lower cost and non-corrosive nature, but with lower tensile strength. Thus, by combining the two types of fibers, the benefits of each material can be used. This work presents the results of an experimental program to evaluate the effect of the hybridization of metallic and synthetic fibers on the shear strength of self-compacting concrete beams without stirrups. The results demonstrate that both steel and hybrid fibers result in greater shear strength compared with the reference concrete without fibers before shear crack formation; however, the greatest advantages are attributed to post-cracking residual strength. The experimental results were compared with estimates calculated using equations published in the literature, demonstrating the feasibility of using some existing equations for concretes with the addition of hybrid fibers.

Abstract This article presents a literature review on the use of flexible formworks for concrete structures, from their earliest conceptions in the late 19th century to recent applications. The challenges faced by builders, engineers, and architects in designing flexible moulds are discussed. A bibliometric analysis on the subject is also presented. Different methodologies for categorization are analyzed, and a consistent classification based on the application technique is proposed. Advantages and disadvantages of the formwork system are discussed and the article concludes with the perspective of future studies on the construction process through computational models.

Strut-and-tie model (STM), with its truss analogy, is yet a powerful tool for analyzing and designing complex prestressed and reinforced concrete structures. Which represents a simplistic method that predominantly provides conservative strength predictions. This article aims to provide a comprehensive review on the STM and its applications in reinforced concrete (RC) deep beams that are available in the open literature. Accordingly, a review of the STM technique is presented with a particular focus on the basic concept of STM, associated B- and D-regions, fundamentals of STM, historical development of STM, and its applications in RC deep beams. The review also covers a wide range of STM aspects including: strut strength and its effectiveness factor, node strength and its effectiveness factor, tie strength and minimum reinforcement, the effect of tie anchorage, and strain energy minimization. Hence, essential formulas and parameters of STM proposed by various design codes and researchers are reviewed and then summarized in comprehensive tables. Consequently, from the reviewed literature, several challenges are highlighted. Findings and recommendations that are necessary to be considered in future works are addressed. With the provided information, this review article serves as a useful reference for structural engineers and researchers who are familiar with STM, as well as who wish to enter this research field.

Non-destructive testing of reinforced concrete structures is of utmost importance in construction and civil engineering, where many factors can cause internal and external deterioration of reinforced concrete structures: the environment, nuclear radiation, and structural defects, among others. Geophysical non-destructive testing methods such as ultrasonic and radar have been increasingly used in civil engineering in recent years. Improvements in the ultrasonic testing of concrete have produced detailed images of the interiors of even the most complex structures and allowed for the earliest possible detection of deterioration. In this study, three geophysical methods were applied to inspect concrete structures: parallel seismic, sonic echo, and ultra-seismic. These three testing methods are applied to concrete foundations of two depths, namely 5, and 7 m; the deep foundation has a fracture at a depth of 4.5 m. The collected data are processed using a low-pass filter to remove the higher frequencies. These results of parallel seismic tests accurately predict foundation depths of 5.0 and 6.6 m and P-wave velocities of 2551 and 1097 m/s, respectively. Sonic echo tests yield depth predictions of 5.07 and 7.01 m for the tested foundations and 4.44 m for the depth of the crack. Meanwhile, ultra-seismic tests give depth predictions of 7.1 and 4.6 m for the foundation and fracture, respectively, while clarifying the P-wave velocities of the corresponding reflections: 3447 m/s exiting the 7-m foundation and 2668 m/s exiting the embedded fracture. Depth estimates based on each method show strong agreement with true depths. In conclusion, these three geophysical methods have great potential to provide quantitative data to drive quality assurance and remediation for concrete structures.

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]

In non-destructive testing, materials, components, or assemblies are inspected, tested, or evaluated without being destroyed. In nondestructive testing, materials, components, and assemblies are evaluated for quality and integrity without damaging them. The use of non-destructive testing (NDT) is a popular method for assessing the strength and durability of existing concrete structures. An ultrasound pulse velocity test and a rebound hammer test are included in the NDT test. An NDT test includes both ultrasound pulse velocity testing and rebound hammer testing. Strength tests are conducted with rebound hammers, while quality tests are conducted with ultrasonic pulse velocity tests. A lab-made concrete cube was tested on three different ages-seven, fourteen, and 28 days-without destroying it. There were 15 cubes that were treated using non-destructive methods. Using both Schmidt rebound hammer and ultrasonic pulse velocity tests, we determined which non-destructive testing method was faster. In addition to surfaces, Schmidt rebound hammers are the toughest tools to use. Rebound number and concrete strength seem to be connected. Schmidt hammers were employed in both vertical and horizontal settings. Concrete’s ultrasonic pulse velocity is mostly determined by its identity and modulus of elasticity, that are motivated by the substances used in making the concrete in addition to its placement, compaction, and curing techniques.

To prevent potential instability the early detection of cracks is imperative due to the prevalent use of concrete in critical infrastructure. Automated techniques leveraging artificial intelligence, machine learning, and deep learning as the traditional manual inspection methods are time-consuming. The existing automated concrete crack detection algorithms, despite recent advancements, face challenges in robustness, particularly in precise crack detection amidst complex backgrounds and visual distractions, while also maintaining low inference times. Therefore, this paper introduces a novel ensemble mechanism based on multiple quantized You Only Look Once version 8 (YOLOv8) models for the detection and segmentation of cracks in concrete structures. The proposed model is tested on different concrete crack datasets yielding enhanced segmentation results with at least 89.62% precision and intersection over a union score of 0.88. Moreover, the inference time per image is reduced to 27 milliseconds which is at least a 5% improvement over other models in the comparison. This is achieved by amalgamating the predictions of the trained models to calculate the final segmentation mask. The noteworthy contributions of this work encompass the creation of a model with low inference time, an ensemble mechanism for robust crack segmentation, and the enhancement of the learning capabilities of crack detection models. The fast inference time of the model renders it appropriate for real-time applications, effectively tackling challenges in infrastructure maintenance and safety.

To investigate the effects of parameters on the seismic performance of slender T-shaped RC walls subjected to a biaxial seismic action, a numerical model was established using a fiber-based cross-section and displacement-based beam–column element. The axial load ratio, shear span ratio, flange width to web height ratio, concrete strength grade, stirrup ratio, and longitudinal reinforcement ratio were selected for the parametric study, and the effects of these parameters on the performance degradation under biaxial loading were investigated. Furthermore, a sensitivity analysis of various parameters for the decrease was conducted. The results showed that the bearing and deformation capacities under biaxial loading were both decreased, and the total energy consumption was greater than that under uniaxial loading. The impacts of different parameters and loading paths on the decrease extent were significantly different, and the overall reduction was greater in the flange direction than in the web direction. Under the square loading path, the T-shaped wall had the greatest reduction in its seismic performance, followed by the eight-shaped and cruciform loading paths. The changes in the axial load ratio, shear span ratio, and concrete strength significantly affected the performance degradation under biaxial loading. Accordingly, it is recommended to reasonably consider the values of these three parameters in a multidimensional seismic design to maintain safety redundancy.

This study investigated the innovative use of magnetoelastic sensors to detect the formation of single cracks in cement beams under bending vibrations. The detection method involved monitoring changes in the bending mode spectrum when a crack was introduced. The sensors, functioning as strain sensors, were placed on the beams, and their signals were detected non-invasively using a nearby detection coil. The beams were simply supported, and mechanical impulse excitation was applied. The recorded spectra displayed three distinct peaks representing different bending modes. The sensitivity for crack detection was determined to be a 24% change in the sensing signal for every 1% decrease in beam volume due to the crack. Factors influencing the spectra were investigated, including pre-annealing of the sensors, which improved the detection signal. The choice of beam support material was also explored, revealing that steel yielded better results than wood. Overall, the experiments demonstrated that magnetoelastic sensors enabled the detection of small cracks and provided qualitative information about their location. [ABSTRACT FROM AUTHOR]

Recent advances in materials science have led to the development of smart materials that can continuously adapt to different loading conditions and changing environment to meet the growing demand for smart structural systems. The unique characteristics of superelastic NiTi shape memory alloys (SMAs) have attracted the attention of structural engineers worldwide. SMAs are metallic materials that can retrieve their original shape upon exposure to various temperatures or loading/unloading conditions with minimal residual deformation. SMAs have found increasing applications in the building industry because of their high strength, high actuation and damping capacities, good durability, and superior fatigue resistance. Despite the research conducted on the structural applications of SMAs during the previous decades, the existing literature lacks reviews on their recent uses in building industry such as prestressing concrete beams, seismic strengthening of footing–column connections, and fiber-reinforced concrete. Furthermore, scarce research exists on their performance under corrosive environments, elevated temperatures, and intensive fires. Moreover, the high manufacturing cost of SMA and the lack of knowledge transfer from research to practice are the main obstacles behind their limited use in concrete structures. This paper sheds light on the latest progress made in the applications of SMA in reinforced concrete structures during the last two decades. In addition, the paper concludes with the recommendations and future opportunities associated with expanding the use of SMA in civil infrastructures. [ABSTRACT FROM AUTHOR]

Corrosion-induced deterioration of the in-service marine reinforced concrete (RC) structures may result in unsatisfactory serviceability or insufficient safety. Surface deterioration analysis based on random fields can provide information regarding the future development of the surface damage of the in-service RC members, but its accuracy needs to be verified in order to broaden its applications in durability assessment. This paper performs an empirical study to verify the accuracy of the surface deterioration analysis based on random fields. The batch-casting effect is considered to establish the "step-shaped" random fields for stochastic parameters in order to better coordinate their actual spatial distributions. Inspection data from a 23-year-old high-pile wharf is obtained and analyzed in this study. The simulation results of the RC panel members' surface deterioration are compared with the in-situ inspection results with respect to the steel cross-section loss, cracking proportion, maximum crack width, and surface damage grades. It shows that the simulation results coordinate well with the inspection results. On this basis, four maintenance options are established and compared in terms of the total amounts of RC panel members needing restoration and the total economic costs. It provides a comparative tool to aid the owners in selecting the optimal maintenance action given the inspection results, to minimize the lifecycle cost and guarantee the sufficient serviceability and safety of the structures. [ABSTRACT FROM AUTHOR]

Cracks in concrete surfaces are one of the most prominent causes of the degradation of concrete structures such as bridges, roads, buildings, etc. Hence, it is very crucial to detect cracks at an early stage to inspect the structural health of the concrete structure. To solve the drawbacks of manual inspection, Image Processing Techniques (IPTs), especially those based on Deep Learning (DL) methods, have been investigated for the past few years. Due to the groundbreaking development of this field, researchers have devoted their endeavors to detecting cracks using DL-based IPTs and as a result, the techniques have given answers to many challenging problems. However, to the best of our knowledge, a state-of-the-art systematic review paper is lacking in this field that would present a scientometric analysis as well as a critical survey of the existing works to document the research trends and summarize the prominent IPTs for detecting cracks in concrete structures. Therefore, this article comes forward to spur researchers with a systematic review of the relevant literature, which will present both scientometric and critical analysis of the papers published in this research area. The scientometric data that are brought out from the articles are analyzed and visualized by using VOSviewer and CiteSpace text mining tools in terms of some parameters. Furthermore, this article elucidates research from all over the world by highlighting and critically analyzing the incarnated essence of some of the most influential papers. Moreover, this research raises some common questions as well as extracts answers from the analyzed papers to highlight various features of the utilized methods. [ABSTRACT FROM AUTHOR]

This article considers the prospects of the application of building structures made of polymer concrete composites on the basis of strength analysis. The issues of application and structure of polymer-concrete mixtures are considered. Features of the stress-strain state of normal sections of polymer concrete beams are revealed. The dependence between the stresses and relative deformations of rubber polymer concretes and beams containing reinforcement frame and fiber reinforcement has been determined. The main direction of the study was the choice of ways to increase the strength characteristics of concrete with the addition of a polymer base and to increase the reliability of structures in general. The paper presents the results of experimental and mathematical studies of the stress-strain state and strength, as well as deflections of reinforced rubber-polymer beams. The peculiarities of fracture of reinforced rubber-polymer beams along their sections have been revealed according to the results of the experiment. The peculiarities of fracture formation of reinforced rubber-polymer beams have also been revealed. The conducted work has shown that the share of longitudinal reinforcement and the height of the fibrous reinforcement zone are the main factors. These reasons determine the characteristics of the strength of the beams and their resistance to destructive influences. The importance and scientific novelty of the work are the identified features of the stress-strain state of normal sections of rubber-concrete beams, namely, it has been established that the ultimate strength in axial compression and tension, deformations corresponding to the ultimate strength for rubber concrete exceed similar parameters for cement concrete 2.5–6.5 times. In the case of the addition of fiber reinforcement, this increase becomes, respectively, 3.0–7.5 times. [ABSTRACT FROM AUTHOR]

The progressive damage monitoring and fracture evaluation of plain concrete beam specimens subjected to three-point bending (TPB) are reported in this paper. The objective of the present research is to systematically correlate the fracture behavior of a concrete beam with acoustic emission (AE) data recorded during the present set of experiments. The damage growth and failure mechanisms of the beam specimens are investigated with AE parametric analysis, b-value analysis, and digital image correlation method. Eventually, a reasonable correlation between AE energy and the released fracture energy is established at various stages of loading, which is confirmed with the set of TPB beam experiments and further validated with nonlinear numerical finite-element analysis. The energy released during the cracking processes at different loading stages in the experiment is quantified and systematically analyzed. Acoustic and fracture energies are dependent on active concrete cracking, and their correlation is helpful in determining the extent of damage in the concrete structures. Such a relationship can be used as an operational tool for structural health monitoring of structures as an early warning system supplemented with numerical analysis, a well-calibrated laboratory, and in situ AE experiments. [ABSTRACT FROM AUTHOR]

In order to study the feasibility of strengthening of segmental tunnel linings by using steel–concrete composites (SCC), a three-dimensional (3D) finite element (FE) model is proposed in this paper. The nonlinear mechanical behavior of concrete is described by a plastic-damage model. The nonlinearity, resulting from the interface of the SCC and reinforced concrete (RC) segments, is simulated with the help of a system of springs. The analysis results are compared with those obtained from a full-scale test of a tunnel segment. Their agreement validates the usefulness of the 3D FE model. Numerical re-analysis of the test shows that the interfacial connectors govern both the strengthening effect of SCC and the failure pattern of the strengthened segments. Thus, the force-transmitting capacity of the interfacial connectors should be concerned in design activities. As regards the circular segments, the interfacial connectors refer to both the shearing and the stripping connectors. The composite effect of the SCC and RC segments increases with the increasing number of these connectors. The latter, therefore, results in the increases of the bearing capacities and stiffnesses of the strengthened segments. Those increases become insignificant as the number of these connectors is sufficient to ensure a perfect composite effect of the SCC and RC segments. In addition, the numerical simulations show that using high-performance steel shell (HPS) or/and ultra-high-performance concrete (UHPC) is an effective way to increase the strengthening effect of SCCs.

The paper is focused on probabilistic assessment and sensitivity analysis of existing prestressed concrete bridge using surrogate model in form of Polynomial Chaos Expansion (PCE). The bridge was selected in the framework of the European Project INTERREG AUSTRIA-CZECH REPUBLIC "TCZ190 SAFEBRIDGE" focused on advanced numerical analysis of existing bridges represented by non-linear finite element model. In this study, surrogate model in form of PCE was created, which represents very efficient type of surrogate model. One of significant advantages of PCE is powerful post-processing including sensitivity and moment analysis of the response, which is important part of probabilistic analysis. The obtained numerical results of advanced stochastic analysis consisting of uncertainty quantification and sensitivity analysis of the existing bridge structure are presented in the paper.

The paper is focused on assessment of existing prestressed concrete bridge by simplified methods for estimation of coefficient of variation. The bridge was selected in the framework of the European Project INTERREG AUSTRIA-CZECH REPUBLIC "ATCZ190 SAFEBRIDGE" focused on advanced numerical analysis of existing bridges represented by non-linear finite element model. The key ingredient in semi-probabilistic design and assessment of structures is an estimation of coefficient of variation (ECoV). Recently, correlation interval approach together with novel Eigen ECoV were proposed by authors of this paper and theoretically proved to be an efficient and accurate alternative to existing methods. This contribution is focused on practical application of Eigen ECoV on real example solved by NLFEM and its comparison with other existing simplified methods.