Special Issue "Artificial Neural Networks Applied in Civil Engineering"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 15 July 2021.

Special Issue Editor

Dr. Nikos D. Lagaros
E-Mail Website
Guest Editor
Institute of Structural Analysis & Antiseismic Research, Department of Structural Engineering, School of Civil Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., Zografou Campus, Athens 157 80, Greece
Interests: structural design optimization; shape and topology optimization; structural health monitoring; artificial intelligence; machine learning; nano modelling; additive manufacturing

Special Issue Information

Dear Colleagues,

In recent years, artificial neural networks (ANN) and artificial intelligence (AI) in general have drawn significant attention with respect to their applications in several scientific fields, varying from big data handling to medical diagnosis. The use of ANN techniques is already present in everyday applications everyone uses, such as personalized ads, virtual assistants, autonomous driving, etc. The breakthrough of ANNs can be traced back to the year 2005 and forward with the proposal of novel learning architectures such as deep convolutional neural networks (CNN) and deep belief networks (DBN), while significant progress has been achieved so far, and new methodologies are being proposed, such as generative adversarial neural networks (GAN). At present, ANN techniques are widely used in several forms of engineering applications.

It is our great pleasure to invite you to contribute to this Special Issue by presenting your results on applications and advances of ANN to civil engineering problems. Papers can focus on applications related to structural engineering, transportation engineering, geotechnical engineering, hydraulic engineering, environmental engineering, coastal and ocean engineering, structural health monitoring, as well as construction management. Articles submitted to this Special Issue could also deal with the most significant recent developments on the topics of ANN and its application in civil engineering. The papers can present modeling, optimization, control, measurements, analysis, and applications.

Dr. Nikos Lagaros
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Deep learning
  • IoT and real-time monitoring
  • Optimization
  • Learning systems
  • Mathematical and computational analysis

Published Papers (13 papers)

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Research

Article
Probabilistic Design of Retaining Wall Using Machine Learning Methods
Appl. Sci. 2021, 11(12), 5411; https://doi.org/10.3390/app11125411 - 10 Jun 2021
Viewed by 268
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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Article
Neural Network-Based Prediction: The Case of Reinforced Concrete Members under Simple and Complex Loading
Appl. Sci. 2021, 11(11), 4975; https://doi.org/10.3390/app11114975 - 28 May 2021
Viewed by 355
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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Article
Experimental Investigation and Artificial Neural Network Based Prediction of Bond Strength in Self-Compacting Geopolymer Concrete Reinforced with Basalt FRP Bars
Appl. Sci. 2021, 11(11), 4889; https://doi.org/10.3390/app11114889 - 26 May 2021
Viewed by 422
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 (db), and are denoted as 5 db, 10 db, and 15 db 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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Article
Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil
Appl. Sci. 2021, 11(11), 4754; https://doi.org/10.3390/app11114754 - 22 May 2021
Viewed by 413
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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Article
An ANN Model for Predicting the Compressive Strength of Concrete
Appl. Sci. 2021, 11(9), 3798; https://doi.org/10.3390/app11093798 - 22 Apr 2021
Viewed by 525
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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Article
Implementation of Machine Learning Algorithms in Spectral Analysis of Surface Waves (SASW) Inversion
Appl. Sci. 2021, 11(6), 2557; https://doi.org/10.3390/app11062557 - 12 Mar 2021
Viewed by 386
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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Article
Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
Appl. Sci. 2021, 11(6), 2458; https://doi.org/10.3390/app11062458 - 10 Mar 2021
Viewed by 356
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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Article
Predicting the Safety Climate in Construction Sites of Saudi Arabia: A Bootstrapped Multiple Ordinal Logistic Regression Modeling Approach
Appl. Sci. 2021, 11(4), 1474; https://doi.org/10.3390/app11041474 - 06 Feb 2021
Cited by 1 | Viewed by 488
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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Article
Numerical Evaluation of Early-Age Crack Induction in Continuously Reinforced Concrete Pavement with Different Saw-Cut Dimensions Subjected to External Varying Temperature Field
Appl. Sci. 2021, 11(1), 42; https://doi.org/10.3390/app11010042 - 23 Dec 2020
Cited by 1 | Viewed by 422
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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Article
Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures
Appl. Sci. 2020, 10(22), 8105; https://doi.org/10.3390/app10228105 - 16 Nov 2020
Cited by 3 | Viewed by 755
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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Article
Study on Two-Phase Fluid-Solid Coupling Characteristics in Saturated Zone of Subgrade Considering the Effects of Fine Particles Migration
Appl. Sci. 2020, 10(21), 7539; https://doi.org/10.3390/app10217539 - 26 Oct 2020
Viewed by 480
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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Article
Joint Extraction of Multiple Relations and Entities from Building Code Clauses
Appl. Sci. 2020, 10(20), 7103; https://doi.org/10.3390/app10207103 - 13 Oct 2020
Viewed by 622
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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Article
Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine
Appl. Sci. 2020, 10(13), 4565; https://doi.org/10.3390/app10134565 - 30 Jun 2020
Cited by 4 | Viewed by 654
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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