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8 pages, 1286 KiB

Open AccessEditorial

Artificial Neural Networks Applied in Civil Engineering

by Nikos D. Lagaros

Appl. Sci. 2023, 13(2), 1131; https://doi.org/10.3390/app13021131 - 14 Jan 2023

Cited by 7 | Viewed by 3462

Abstract

In recent years, artificial neural networks (ANN) and artificial intelligence (AI), in general, have garnered significant attention with respect to their applications in several scientific fields, varying from big data management to medical diagnosis [...] Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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Research

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21 pages, 5835 KiB

Open AccessArticle

A Deep Learning-Based Integration Method for Hybrid Seismic Analysis of Building Structures: Numerical Validation

by Nabil Mekaoui and Taiki Saito

Appl. Sci. 2022, 12(7), 3266; https://doi.org/10.3390/app12073266 - 23 Mar 2022

Cited by 5 | Viewed by 2453

Abstract

A hybrid seismic analysis computing the full nonlinear response of building structures is proposed and validated in this paper. Recurrent neural networks are trained to predict the nonlinear hysteretic response of isolation devices with deformation- and velocity-dependent behavior. Then, they are implemented in [...] Read more.

A hybrid seismic analysis computing the full nonlinear response of building structures is proposed and validated in this paper. Recurrent neural networks are trained to predict the nonlinear hysteretic response of isolation devices with deformation- and velocity-dependent behavior. Then, they are implemented in an explicit time integration method for time history analysis. A comprehensive framework is proposed to develop and test deep learning models considering the data framing, the network architecture, and the learning behavior. Hybrid seismic analyses of three base-isolated building models subjected to four ground motions with different properties were performed in order to check their efficiency. The small relative errors of computed results to those of the conventional analysis successfully validate the accuracy of the proposed analysis. Its computation time depends mainly on the ground motion duration and is considered negligible. The development of the machine learning model is more time-consuming but nonrepetitive since it can be saved and reused to analyze any new structure containing the same target components. The proposed hybrid seismic analysis overcomes the shortcomings of usual applications of machine learning in structural response prediction problems being limited to specific response quantity(s) of the same structure(s) used at the training process. By taking advantage of both mechanics-based and data-driven methods, results reveal that hybrid analysis is an efficient tool for building-response simulation. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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19 pages, 1260 KiB

Open AccessFeature PaperArticle

Deep Learning-Based Accuracy Upgrade of Reduced Order Models in Topology Optimization

by Nikos Ath. Kallioras, Alexandros N. Nordas and Nikos D. Lagaros

Appl. Sci. 2021, 11(24), 12005; https://doi.org/10.3390/app112412005 - 16 Dec 2021

Cited by 7 | Viewed by 2350

Abstract

Topology optimization problems pose substantial requirements in computing resources, which become prohibitive in cases of large-scale design domains discretized with fine finite element meshes. A Deep Learning-assisted Topology OPtimization (DLTOP) methodology was previously developed by the authors, which employs deep learning techniques to [...] Read more.

Topology optimization problems pose substantial requirements in computing resources, which become prohibitive in cases of large-scale design domains discretized with fine finite element meshes. A Deep Learning-assisted Topology OPtimization (DLTOP) methodology was previously developed by the authors, which employs deep learning techniques to predict the optimized system configuration, thus substantially reducing the required computational effort of the optimization algorithm and overcoming potential bottlenecks. Building upon DLTOP, this study presents a novel Deep Learning-based Model Upgrading (DLMU) scheme. The scheme utilizes reduced order (surrogate) modeling techniques, which downscale complex models while preserving their original behavioral characteristics, thereby reducing the computational demand with limited impact on accuracy. The novelty of DLMU lies in the employment of deep learning for extrapolating the results of optimized reduced order models to an optimized fully refined model of the design domain, thus achieving a remarkable reduction of the computational demand in comparison with DLTOP and other existing techniques. The effectiveness, accuracy and versatility of the novel DLMU scheme are demonstrated via its application to a series of benchmark topology optimization problems from the literature. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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20 pages, 90783 KiB

Open AccessArticle

Multiple-Input Convolutional Neural Network Model for Large-Scale Seismic Damage Assessment of Reinforced Concrete Frame Buildings

by Chen Xiong, Jie Zheng, Liangjin Xu, Chengyu Cen, Ruihao Zheng and Yi Li

Appl. Sci. 2021, 11(17), 8258; https://doi.org/10.3390/app11178258 - 06 Sep 2021

Cited by 15 | Viewed by 2538

Abstract

This study introduces a multiple-input convolutional neural network (MI-CNN) model for the seismic damage assessment of regional buildings. First, ground motion sequences together with building attribute data are adopted as inputs of the proposed MI-CNN model. Second, the prediction accuracy of MI-CNN model [...] Read more.

This study introduces a multiple-input convolutional neural network (MI-CNN) model for the seismic damage assessment of regional buildings. First, ground motion sequences together with building attribute data are adopted as inputs of the proposed MI-CNN model. Second, the prediction accuracy of MI-CNN model is discussed comprehensively for different scenarios. The overall prediction accuracy is 79.7%, and the prediction accuracies for all scenarios are above 77%, indicating a good prediction performance of the proposed method. The computation efficiency of the proposed method is 340 times faster than that of the nonlinear multi-degree-of-freedom shear model using time history analysis. Third, a case study is conducted for reinforced concrete (RC) frame buildings in Shenzhen city, and two seismic scenarios (i.e., M6.5 and M7.5) are studied for the area. The simulation results of the area indicate a good agreement between the MI-CNN model and the benchmark model. The outcomes of this study are expected to provide a useful reference for timely emergency response and disaster relief after earthquakes. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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10 pages, 1967 KiB

Open AccessArticle

Predicting the Compressive Strength of Concrete Using an RBF-ANN Model

by Nan-Jing Wu

Appl. Sci. 2021, 11(14), 6382; https://doi.org/10.3390/app11146382 - 10 Jul 2021

Cited by 23 | Viewed by 2336

Abstract

In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in [...] Read more.

In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic gradient approach presented in the textbook is employed for determining the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs have converged to the solutions of global minimum error. So, the only consideration of whether the ANN can work in practical uses is just the issue of over-fitting. The ANN with only three RBFs is finally chosen. The results of verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author’s previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using the formulae expressed in this article, anyone can predict the 28-day compressive strength of concrete according to the concrete mix proportioning on his/her own. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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20 pages, 4169 KiB

Open AccessArticle

Evaluation of Deep Learning against Conventional Limit Equilibrium Methods for Slope Stability Analysis

by Behnam Azmoon, Aynaz Biniyaz and Zhen (Leo) Liu

Appl. Sci. 2021, 11(13), 6060; https://doi.org/10.3390/app11136060 - 29 Jun 2021

Cited by 9 | Viewed by 2474

Abstract

This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to calculate the [...] Read more.

This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to calculate the factor of safety (FS) using four limit equilibrium methods: Bishop’s simplified method, the Fellenius method, Janbu’s simplified method, and Janbu’s corrected method. The code was verified against Slide2 in RocScience. Subsequently, the average FS values were used to approximate the “true” FS of the slopes for labeling the images for deep learning. Using this code, a comprehensive dataset of slope images with wide ranges of geometries and soil properties was created. The average FS values were used to label the images for implementing two deep learning models: a multiclass classification and a regression model. After training, the deep learning models were used to predict the FS of an independent set of slope images. Finally, the performance of the models was compared to that of the conventional methods. This study found that deep learning methods can reach accuracies as high as 99.71% while improving computational efficiency by more than 18 times compared with conventional methods. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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25 pages, 60087 KiB

Open AccessArticle

Prediction of Change Rate of Settlement for Piled Raft Due to Adjacent Tunneling Using Machine Learning

by Dong-Wook Oh, Suk-Min Kong, Yong-Joo Lee and Heon-Joon Park

Appl. Sci. 2021, 11(13), 6009; https://doi.org/10.3390/app11136009 - 28 Jun 2021

Cited by 5 | Viewed by 1619

Abstract

For tunneling in urban areas, understanding the interaction and behavior of tunnels and the foundation of adjacent structures is very important, and various studies have been conducted. Superstructures in urban areas are designed and constructed with piled rafts, which are more effective than [...] Read more.

For tunneling in urban areas, understanding the interaction and behavior of tunnels and the foundation of adjacent structures is very important, and various studies have been conducted. Superstructures in urban areas are designed and constructed with piled rafts, which are more effective than the conventional piled foundation. However, the settlement of a piled raft induced by tunneling mostly focuses on raft settlement. In this study, therefore, raft and pile settlements were obtained through 3D numerical analysis, and the change rate of settlement along the pile length was calculated by linear assumption. Machine learning was utilized to develop prediction models for raft and pile settlement and change rate of settlement along the pile length due to tunneling. In addition, raft settlement in the laboratory model test was used for the verification of the prediction model of raft settlement, derived through machine learning. As a result, the change rate of settlement along the pile length was between 0.64 and −0.71. In addition, among features, horizontal offset pile tunnel had the greatest influence, and pile diameter and number had relatively little influence. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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14 pages, 1308 KiB

Open AccessArticle

Probabilistic Design of Retaining Wall Using Machine Learning Methods

by Pratishtha Mishra, Pijush Samui and Elham Mahmoudi

Appl. Sci. 2021, 11(12), 5411; https://doi.org/10.3390/app11125411 - 10 Jun 2021

Cited by 14 | Viewed by 3598

Abstract

Retaining walls are geostructures providing permanent lateral support to vertical slopes of soil, and it is essential to analyze the failure probability of such a structure. To keep the importance of geotechnics on par with the advancement in technology, the implementation of artificial [...] Read more.

Retaining walls are geostructures providing permanent lateral support to vertical slopes of soil, and it is essential to analyze the failure probability of such a structure. To keep the importance of geotechnics on par with the advancement in technology, the implementation of artificial intelligence techniques is done for the reliability analysis of the structure. Designing the structure based on the probability of failure leads to an economical design. Machine learning models used for predicting the factor of safety of the wall are Emotional Neural Network, Multivariate Adaptive Regression Spline, and SOS–LSSVM. The First-Order Second Moment Method is used for calculating the reliability index of the wall. In addition, these models are assessed based on the results they produce, and the best model among these is concluded for extensive field study in the future. The overall performance evaluation through various accuracy quantification determined SOS–LSSVM as the best model. The obtained results show that the reliability index calculated by the AI methods differs from the reference values by less than 2%. These methodologies have made the problems facile by increasing the precision of the result. Artificial intelligence has removed the cumbersome calculations in almost all the acquainted fields and disciplines. The techniques used in this study are evolved versions of some older algorithms. This work aims to clarify the probabilistic approach toward designing the structures, using the artificial intelligence to simplify the practical evaluations. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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32 pages, 36908 KiB

Open AccessArticle

Neural Network-Based Prediction: The Case of Reinforced Concrete Members under Simple and Complex Loading

by Afaq Ahmad, Nikos D. Lagaros and Demetrios M. Cotsovos

Appl. Sci. 2021, 11(11), 4975; https://doi.org/10.3390/app11114975 - 28 May 2021

Cited by 7 | Viewed by 1964

Abstract

The objective of this study is to compare conventional models used for estimating the load carrying capacity of reinforced concrete (RC) members, i.e., Current Design Codes (CDCs), with the method based on different assumptions, i.e., the Compressive Force Path (CFP) method and a [...] Read more.

The objective of this study is to compare conventional models used for estimating the load carrying capacity of reinforced concrete (RC) members, i.e., Current Design Codes (CDCs), with the method based on different assumptions, i.e., the Compressive Force Path (CFP) method and a non-conventional problem solver, i.e., an Artificial Neural Network (ANN). For this purpose, four different databases with the details of the critical parameters of (i) RC beams in simply supported conditions without transverse steel or stirrups (BWOS) and RC beams in simply supported conditions with transverse steel or stirrups (BWS), (ii) RC columns with cantilever-supported conditions (CWA), (iii) RC T-beams in simply supported conditions without transverse steel or stirrups (TBWOS) and RC T-beams in simply supported conditions with transverse steel or stirrups (TBWS) and (iv) RC flat slabs in simply supported conditions under a punching load (SCS) are developed based on the data from available experimental studies. These databases obtained from the published experimental studies helped us to estimate the member response at the ultimate limit-state (ULS). The results show that the predictions of the CFP and the ANNs often correlate closer to the experimental data as compared to the CDCs. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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25 pages, 12733 KiB

Open AccessEditor’s ChoiceArticle

Experimental Investigation and Artificial Neural Network Based Prediction of Bond Strength in Self-Compacting Geopolymer Concrete Reinforced with Basalt FRP Bars

by Sherin Khadeeja Rahman and Riyadh Al-Ameri

Appl. Sci. 2021, 11(11), 4889; https://doi.org/10.3390/app11114889 - 26 May 2021

Cited by 19 | Viewed by 2652

Abstract

The current research on concrete and cementitious materials focuses on finding sustainable solutions to address critical issues, such as increased carbon emissions, or corrosion attack associated with reinforced concrete structures. Geopolymer concrete is considered to be an eco-friendly alternative due to its superior [...] Read more.

The current research on concrete and cementitious materials focuses on finding sustainable solutions to address critical issues, such as increased carbon emissions, or corrosion attack associated with reinforced concrete structures. Geopolymer concrete is considered to be an eco-friendly alternative due to its superior properties in terms of reduced carbon emissions and durability. Similarly, the use of fibre-reinforced polymer (FRP) bars to address corrosion attack in steel-reinforced structures is also gaining momentum. This paper investigates the bond performance of a newly developed self-compacting geopolymer concrete (SCGC) reinforced with basalt FRP (BFRP) bars. This study examines the bond behaviour of BFRP-reinforced SCGC specimens with variables such as bar diameter (6 mm and 10 mm) and embedment lengths. The embedment lengths adopted are 5, 10, and 15 times the bar diameter (d_b), and are denoted as 5 d_b, 10 d_b, and 15 d_b throughout the study. A total of 21 specimens, inclusive of the variable parameters, are subjected to direct pull-out tests in order to assess the bond between the rebar and the concrete. The result is then compared with the SCGC reinforced with traditional steel bars, in accordance with the ACI 440.3R-04 and CAN/CSA-S806-02 guidelines. A prediction model for bond strength has been proposed using artificial neural network (ANN) tools, which contributes to the new knowledge on the use of Basalt FRP bars as internal reinforcement in an ambient-cured self-compacting geopolymer concrete. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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13 pages, 24147 KiB

Open AccessArticle

Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil

by Assia Aboubakar Mahamat, Moussa Mahamat Boukar, Nurudeen Mahmud Ibrahim, Tido Tiwa Stanislas, Numfor Linda Bih, Ifeyinwa Ijeoma Obianyo and Holmer Savastano, Jr.

Appl. Sci. 2021, 11(11), 4754; https://doi.org/10.3390/app11114754 - 22 May 2021

Cited by 13 | Viewed by 2259

Abstract

Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. Often, the determination of their properties is conducted based on a conventional [...] Read more.

Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. Often, the determination of their properties is conducted based on a conventional materials procedure. Hence, there is inaccuracy in understanding the properties of the unconventional materials. To obtain more accurate properties, a support vector machine (SVM), artificial neural network (ANN) and linear regression (LR) were used to predict the compressive strength of the alkali-activated termite soil. In this study, factors such as activator concentration, Si/Al, initial curing temperature, water absorption, weight and curing regime were used as input parameters due to their significant effect in the compressive strength. The experimental results depict that SVM outperforms ANN and LR in terms of R² score and root mean square error (RMSE). Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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13 pages, 2086 KiB

Open AccessArticle

An ANN Model for Predicting the Compressive Strength of Concrete

by Chia-Ju Lin and Nan-Jing Wu

Appl. Sci. 2021, 11(9), 3798; https://doi.org/10.3390/app11093798 - 22 Apr 2021

Cited by 50 | Viewed by 5212

Abstract

An artificial neural network (ANN) model for predicting the compressive strength of concrete is established in this study. The Back Propagation (BP) network with one hidden layer is chosen as the structure of the ANN. The database of real concrete mix proportioning listed [...] Read more.

An artificial neural network (ANN) model for predicting the compressive strength of concrete is established in this study. The Back Propagation (BP) network with one hidden layer is chosen as the structure of the ANN. The database of real concrete mix proportioning listed in earlier research by another author is used for training and testing the ANN. The proper number of neurons in the hidden layer is determined by checking the features of over-fitting while the synaptic weights and the thresholds are finalized by checking the features of over-training. After that, we use experimental data from other papers to verify and validate our ANN model. The final result of the synaptic weights and the thresholds in the ANN are all listed. Therefore, with them, and using the formulae expressed in this article, anyone can predict the compressive strength of concrete according to the mix proportioning on his/her own. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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24 pages, 10632 KiB

Open AccessArticle

Implementation of Machine Learning Algorithms in Spectral Analysis of Surface Waves (SASW) Inversion

by Sadia Mannan Mitu, Norinah Abd. Rahman, Khairul Anuar Mohd Nayan, Mohd Asyraf Zulkifley and Sri Atmaja P. Rosyidi

Appl. Sci. 2021, 11(6), 2557; https://doi.org/10.3390/app11062557 - 12 Mar 2021

Cited by 6 | Viewed by 2667

Abstract

One of the complex processes in spectral analysis of surface waves (SASW) data analysis is the inversion procedure. An initial soil profile needs to be assumed at the beginning of the inversion analysis, which involves calculating the theoretical dispersion curve. If the assumption [...] Read more.

One of the complex processes in spectral analysis of surface waves (SASW) data analysis is the inversion procedure. An initial soil profile needs to be assumed at the beginning of the inversion analysis, which involves calculating the theoretical dispersion curve. If the assumption of the starting soil profile model is not reasonably close, the iteration process might lead to nonconvergence or take too long to be converged. Automating the inversion procedure will allow us to evaluate the soil stiffness properties conveniently and rapidly by means of the SASW method. Multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and linear regression (LR) algorithms were implemented in order to automate the inversion. For this purpose, the dispersion curves obtained from 50 field tests were used as input data for all of the algorithms. The results illustrated that SVR algorithms could potentially be used to estimate the shear wave velocity of soil. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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27 pages, 5304 KiB

Open AccessArticle

Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions

by Ronald Roberts, Laura Inzerillo and Gaetano Di Mino

Appl. Sci. 2021, 11(6), 2458; https://doi.org/10.3390/app11062458 - 10 Mar 2021

Cited by 4 | Viewed by 2219

Abstract

Road networks are critical infrastructures within any region and it is imperative to maintain their conditions for safe and effective movement of goods and services. Road Management, therefore, plays a key role to ensure consistent efficient operation. However, significant resources are required to [...] Read more.

Road networks are critical infrastructures within any region and it is imperative to maintain their conditions for safe and effective movement of goods and services. Road Management, therefore, plays a key role to ensure consistent efficient operation. However, significant resources are required to perform necessary maintenance activities to achieve and maintain high levels of service. Pavement maintenance can typically be very expensive and decisions are needed concerning planning and prioritizing interventions. Data are key towards enabling adequate maintenance planning but in many instances, there is limited available information especially in small or under-resourced urban road authorities. This study develops a roadmap to help these authorities by using flexible data analysis and deep learning computational systems to highlight important factors within road networks, which are used to construct models that can help predict future intervention timelines. A case study in Palermo, Italy was successfully developed to demonstrate how the techniques could be applied to perform appropriate feature selection and prediction models based on limited data sources. The workflow provides a pathway towards more effective pavement maintenance management practices using techniques that can be readily adapted based on different environments. This takes another step towards automating these practices within the pavement management system. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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16 pages, 1085 KiB

Open AccessArticle

Predicting the Safety Climate in Construction Sites of Saudi Arabia: A Bootstrapped Multiple Ordinal Logistic Regression Modeling Approach

by Anas A. Makki and Ibrahim Mosly

Appl. Sci. 2021, 11(4), 1474; https://doi.org/10.3390/app11041474 - 06 Feb 2021

Cited by 7 | Viewed by 2845

Abstract

Construction site accidents can be reduced through proactive steps using prediction models developed based on factors that influence the safety climate. In this study, a prediction model of the safety climate observed by construction site personnel in Saudi Arabia was developed, identifying a [...] Read more.

Construction site accidents can be reduced through proactive steps using prediction models developed based on factors that influence the safety climate. In this study, a prediction model of the safety climate observed by construction site personnel in Saudi Arabia was developed, identifying a set of significant safety climate predictors. The model was built with data collected from 401 active construction site personnel using a bootstrapped multiple ordinal logistic regression model. The model revealed five significant predictors: supervision, guidance, and inspection; social security and health insurance; management’s commitment to safety; management’s safety justice; and coworker influence. The model can correctly predict 67% of the safety evaluations. The identified predictors present proof of the importance of safety support, commitment, and interaction in construction sites and their influence on the perceived evaluations of the safety climate by personnel. Moreover, the prediction model can help construction industry decision makers, safety policy designers, government agencies, and stakeholders to estimate the safety climate and assess the current situation. Furthermore, the model can help form a better understanding and determine areas of improvement, which can translate into higher safety performance levels. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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19 pages, 3434 KiB

Open AccessArticle

Numerical Evaluation of Early-Age Crack Induction in Continuously Reinforced Concrete Pavement with Different Saw-Cut Dimensions Subjected to External Varying Temperature Field

by Muhammad Kashif, Ahsan Naseem, Nouman Iqbal, Pieter De Winne and Hans De Backer

Appl. Sci. 2021, 11(1), 42; https://doi.org/10.3390/app11010042 - 23 Dec 2020

Cited by 4 | Viewed by 2149

Abstract

Since 1970, continuously reinforced concrete pavements have been used in Belgium. The standard design concept for CRCP has been modified through several changes made in the design parameters to eliminate the cluster of closely spaced crack patterns, since these crack patterns lead to [...] Read more.

Since 1970, continuously reinforced concrete pavements have been used in Belgium. The standard design concept for CRCP has been modified through several changes made in the design parameters to eliminate the cluster of closely spaced crack patterns, since these crack patterns lead to the development of spalling and punch-out distresses in CRCPs. Despite adjusting the longitudinal reinforcement ratio, slab thickness, and addition of asphalt interlayer, the narrowly spaced cracks could not be effectively removed. The application of transverse partial surface saw-cuts significantly reduced the probability of randomly occurring cracks in the reconstruction project of the Motorway E313 in Herentals, Belgium. The field investigation has also indicated that the early-age crack induction in CRCP is quite susceptible to the saw-cut depth. Therefore, the present study aims to evaluate the effect of different depths and lengths of the partial surface saw-cut on the effectiveness of crack induction in CRCP under external varying temperature field. For this purpose, the FE software program DIANA 10.3 is used to develop the three dimensional finite element model of the active crack control CRCP segment. The characteristics of early-age crack induction in terms of crack initiation and crack propagation obtained from the FE model are compared and discussed concerning the field observations of the crack development on the active crack control E313 test sections. Findings indicate that the deeper saw-cut with longer cut-lengths could be a more effective attempt to induce the cracks in CRCP in desirable distributions to decrease the risk of spalling and punch-out distresses in the long-term performance of CRCP. These findings could be used as guidance to select the appropriate depth and length of saw-cut for active crack control sections of CRCP in Belgium. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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13 pages, 3140 KiB

Open AccessArticle

Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures

by Jung Jin Kim, Ah-Ram Kim and Seong-Won Lee

Appl. Sci. 2020, 10(22), 8105; https://doi.org/10.3390/app10228105 - 16 Nov 2020

Cited by 44 | Viewed by 4831

Abstract

The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, [...] Read more.

The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and analyze the characteristics of the cracks objectively. In this study, an image analysis technique using deep learning is developed to detect cracks and analyze characteristics (e.g., length, and width) in images for small-scale facilities. Three stages of image processing pipeline are proposed to obtain crack detection and its characteristics. In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. After deep learning-based detection, in the third stage, thinning and tracking algorithms are applied to analyze length and width of crack in the image. The performance of the proposed method was tested using various crack images with label and the results showed good performance of crack detection and its measurement. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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22 pages, 5745 KiB

Open AccessArticle

Study on Two-Phase Fluid-Solid Coupling Characteristics in Saturated Zone of Subgrade Considering the Effects of Fine Particles Migration

by Yu Ding, Jia-sheng Zhang, Yu Jia, Xiao-bin Chen, Xuan Wang and Fei Meng

Appl. Sci. 2020, 10(21), 7539; https://doi.org/10.3390/app10217539 - 26 Oct 2020

Cited by 6 | Viewed by 1990

Abstract

The fluid seepage in saturated zone of subgrade promotes the migration of fine particles in the filler, resulting in the change of pore structure and morphology of the filler and the deformation of solid skeleton, which affects the fluid seepage characteristics. Repeatedly, the [...] Read more.

The fluid seepage in saturated zone of subgrade promotes the migration of fine particles in the filler, resulting in the change of pore structure and morphology of the filler and the deformation of solid skeleton, which affects the fluid seepage characteristics. Repeatedly, the muddy interlayer, mud pumping, and other diseases are finally formed. Based on the theory of two-phase seepage, the theory of porous media seepage, and the principle of effective stress in porous media, a two-phase fluid-solid coupling mathematical model in saturated zone of subgrade considering the effects of fine particles migration is established. The mathematical model is numerically calculated with the software COMSOL Multiphysics^®. The two-phase seepage characteristics and the deformation characteristics of the solid skeleton in saturated zone of the subgrade are studied. The research results show that the volume fraction of fine particles first increases then decreases and finally becomes stable with the increase of time, due to the continuous erosion and migration of fine particles in saturated zone of the subgrade. The volume fraction of fine particles for the upper part of the subgrade is larger than that for the lower part of the subgrade. The porosity, the velocity of fluid, the velocity of fine particles, and the permeability show a trend of increasing first and then stabilizing with time; the pore water pressure has no significant changes with time. The vertical displacement increases first and then decreases slightly with the increase of time, and finally tends to be stable. For the filler with a larger initial volume fraction of fine particles, the maximum value of the volume fraction of fine particles caused by fluid seepage is larger, and the time required to reach the maximum value is shorter. It can be concluded that the volume fraction of fine particles in the subgrade filler should be minimized on the premise that the filler gradation meets the requirements of the specification in actual engineering. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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18 pages, 1985 KiB

Open AccessArticle

Joint Extraction of Multiple Relations and Entities from Building Code Clauses

by Fulin Li, Yuanbin Song and Yongwei Shan

Appl. Sci. 2020, 10(20), 7103; https://doi.org/10.3390/app10207103 - 13 Oct 2020

Cited by 16 | Viewed by 2700

Abstract

The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to address the complexity of [...] Read more.

The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to address the complexity of the semantic relations between named entities. In particular, the existence of two or more overlapping relations involving the same entity greatly exacerbates the difficulty of information extraction. In this paper, a joint extraction model is proposed to extract the relations among entities in the form of triplets. In the proposed model, a hybrid deep learning algorithm combined with a decomposition strategy is applied. First, all candidate subject entities are identified, and then, the associated object entities and predicate relations are simultaneously detected. In this way, multiple relations, especially overlapping relations, can be extracted. Furthermore, nonrelated pairs are excluded through the judicious recognition of subject entities. Moreover, a collection of domain-specific entity and relation types is investigated for model implementation. The experimental results indicate that the proposed model is promising for extracting multiple relations and entities from building codes. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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14 pages, 3378 KiB

Open AccessArticle

Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine

by Manuel Saldaña, Javier González, Ignacio Pérez-Rey, Matías Jeldres and Norman Toro

Appl. Sci. 2020, 10(13), 4565; https://doi.org/10.3390/app10134565 - 30 Jun 2020

Cited by 18 | Viewed by 2368

Abstract

In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. [...] Read more.

In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. The UCS can also be estimated from rock index properties, such as the effective porosity, density, and P-wave velocity. In the case of a porous rock such as travertine, the random distribution of voids inside the test specimen (not detectable in the density-porosity test, but in the compressive strength test) causes large variations on the UCS value, which were found in the range of 62 MPa for this rock. This fact complicates a sufficiently accurate determination of experimental results, also affecting the estimations based on regression analyses. Aiming to solve this problem, statistical analysis, and machine learning models (artificial neural network) was developed to generate a reliable predictive model, through which the best results for a multiple regression model between uniaxial compressive strength (UCS), P-wave velocity and porosity were obtained. Full article

(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)

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