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1 – 10 of 554Jinliang Liu, Yanmin Jia, Guanhua Zhang and Jiawei Wang
During service period, due to the overload or other non-load factors, diagonal cracks of the pre-stressed concrete beam are seriously affecting the safety of the bridge structure…
Abstract
Purpose
During service period, due to the overload or other non-load factors, diagonal cracks of the pre-stressed concrete beam are seriously affecting the safety of the bridge structure. The purpose of this paper is to quickly realize the shear bearing capacity and shear stiffness through maximum width of the diagonal cracks and make correct judgments.
Design/methodology/approach
Through the shear failure test of four test beams, collecting data of diagonal cracks and shear stiffness loss value. According to the deformation curve of the shear stiffness, and combined with the calculation formula of the maximum width of diagonal cracks, the formula for calculating the effective shear stiffness based on the maximum width of diagonal cracks is deduced, then the results are verified by test data. Data regression method is used to establish the effective shear stiffness loss ratio calculation formula, the maximum width of diagonal cracks used as a variable factor, and the accuracy of this formula is verified by comparing the shear failure test results of pre-stressed hollow plates.
Findings
With the increase in width of the diagonal crack, the loss rate of shear stiffness of the concrete beams is initially fast and then becomes slow. The calculation formulae for shear stiffness based on the maximum width of the diagonal cracks were deduced, and the feasibility and accuracy of the formulae were verified by analysis and calculation of shear test data.
Originality/value
A method for quickly determine the shear stiffness loss of structures by using maximum width of the diagonal cracks is established, and using this method, engineers can quickly determine effective shear stiffness loss ratio, without complex calculations. So this method not only ensures the safety of human life, but also saves money.
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Faris Elghaish, Sandra Matarneh, Essam Abdellatef, Farzad Rahimian, M. Reza Hosseini and Ahmed Farouk Kineber
Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly…
Abstract
Purpose
Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.
Design/methodology/approach
To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.
Findings
The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.
Practical implications
With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.
Originality/value
The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.
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Faris Elghaish, Saeed Talebi, Essam Abdellatef, Sandra T. Matarneh, M. Reza Hosseini, Song Wu, Mohammad Mayouf, Aso Hajirasouli and The-Quan Nguyen
This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as…
Abstract
Purpose
This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates.
Design/methodology/approach
A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates.
Findings
The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm.
Practical implications
The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches.
Originality/value
A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.
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R. de Borst and P. Nauta
A new model for handling non‐orthogonal cracks within the smeared crack concept is described. It is based on a decomposition of the total strain increment into a concrete and into…
Abstract
A new model for handling non‐orthogonal cracks within the smeared crack concept is described. It is based on a decomposition of the total strain increment into a concrete and into a crack strain increment. This decomposition also permits a proper combination of crack formation with other non‐linear phenomena such as plasticity and creep and with thermal effects and shrinkage. Relations are elaborated with some other crack models that are currently used for the analysis of concrete structures. The model is applied to some problems involving shear failures of reinforced concrete structures such as a moderately deep beam and an axisymmetric slab. The latter example is also of interest in that it confirms statements that ‘reduced integration’ is not reliable for problems involving crack formation and in that it supports the assertion that identifying numerical divergence with structural failure may be highly misleading.
Aminuddin Suhaimi, Izni Syahrizal Ibrahim and Mariyana Aida Ab Kadir
This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to…
Abstract
Purpose
This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.
Design/methodology/approach
This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.
Findings
The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.
Originality/value
The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.
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This paper aims to study the effect of external glass fibre reinforced polymer (GFRP) plates on the flexural and shear behaviour of structurally deficient reinforced concrete (RC…
Abstract
This paper aims to study the effect of external glass fibre reinforced polymer (GFRP) plates on the flexural and shear behaviour of structurally deficient reinforced concrete (RC) beams, a total of ten 180mm×250mm×2,500mm beams, including over‐designed, unplated under‐designed and plated under‐designed, were tested under four‐point bending condition. Experimental results indicate that the use of GFRP plates enhances the strength and deformation capacity of RC beams by altering their failure modes. Application of side plates on shear‐deficient RC beams appears to be more effective than using bottom plates on flexure‐deficient RC beams. However, without any improvement of concrete compressive capacity, additional shear capacities provided to the beams under the action of side plates increase the likelihood of beam failure by concrete crushing. Simultaneous use of bottom and side plates on flexural‐ and shear‐deficient RC beams may result in reduced deflection.
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Cracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the…
Abstract
Purpose
Cracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.
Design/methodology/approach
This paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.
Findings
It was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.
Originality/value
Literature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.
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Sabiha Barour, Abdesselam Zergua, Farid Bouziadi and Waleed Abed Jasim
This paper aims to develop a non-linear finite element model predicting the response of externally strengthened beams under a three-point flexure test.
Abstract
Purpose
This paper aims to develop a non-linear finite element model predicting the response of externally strengthened beams under a three-point flexure test.
Design/methodology/approach
The ANSYS software is used for modeling. SOILD65, LINK180, SHELL181 and SOLID185 elements are used, respectively, to model concrete, steel reinforcement, polymer and steel plate support. A parametric study was carried out. The effects of compressive strength, Young’s modulus, layers number and carbon fiber-reinforced polymer thickness on beam behavior are analyzed. A comparative study between the non-linear finite element and analytical models, including the ACI 440.2 R-08 model, and experimental data is also carried out.
Findings
A comparative study of the non-linear finite element results with analytical models, including the ACI 440.2 R-08 model and experimental data for different parameters, shows that the strengthened beams possessed better resistance to cracks. In general, the finite element model’s results are in good agreement with the experimental test data.
Practical implications
This model will predict the strengthened beams behavior and can describe the beams physical conditions, yielding the results that can be interpreted in the structural study context without using a laboratory testing.
Originality/value
On the basis of the results, a good match is found between the model results and experimental data at all stages of loading the tested samples. Crack models obtained in the non-linear finite element model in the beams are also presented. The submitted finite element model can be used to predict the behavior of the reinforced concrete beam. Also, the comparative study between an analytical model proposed by of current code of ACI 440.2 R-08 and finite element analysis is investigated.
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Yijiang Peng, Zhenghao Wu, Liping Ying and Desi Yang
This paper aims to propose the five-phase sphere equivalent model of recycled concrete, which can be used to deduce the theoretical formulas for the Poisson’s ratio and effective…
Abstract
Purpose
This paper aims to propose the five-phase sphere equivalent model of recycled concrete, which can be used to deduce the theoretical formulas for the Poisson’s ratio and effective elastic modulus.
Design/methodology/approach
At a mesoscopic level, the equivalent model converts the interfacial layer, which consists of the new interfacial transition zone (ITZ), the old mortar and the old (ITZ), into a uniform equivalent medium. This paper deduces a strength expression for the interfacial transition zone at the microscopic level using the equivalent model and elastic theory. In addition, a new finite element method called the base force element method was used in this research.
Findings
Through numerical simulation, it was found that the mechanical property results from the five-phase sphere equivalent model were in good agreement with those of the random aggregate model. Furthermore, the proposed model agree on quite well with the available experimental data.
Originality/value
The equivalent model can eliminate the influence of the interfacial layer on the macroscopic mechanical properties, thereby improving the calculation accuracy and computational efficiency. The proposed model can also provide a suitable model for multi-scale calculations.
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