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Application of Artificial Neural Networks for Seismic Design and Assessment
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Application of Artificial Neural Networks for Seismic Design and Assessment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 19862

Special Issue Editors

Dr. Konstantinos Morfidis

E-Mail Website
Guest Editor
Division of Earthquake Engineering, Institute of Engineering Seismology and Earthquake Engineering, Thessaloniki, 55535 Pylaia, Greece
Interests: His research interests are in the areas of statics and dynamics of structures, earthquake engineering, software development in the field of (linear and non-linear) finite element method, modeling of engineering problems using artificial intelligence, soil–structure interaction effects and in the improvement of seismic codes in the field of the modeling and analysis of structures.
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Kostinakis

E-Mail Website
Guest Editor
School of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: earthquake engineering; seismic analysis and design; seismic assessment; seismic codes; machine learning; artificial neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The design of new structures with high safety against strong seismic events, as well as the seismic assessment and rehabilitation of existing ones, is one of the most significant research issues in the field of civil engineering. In recent decades, the effectiveness of research in earthquake engineering has risen with the aid of the increasingly developed abilities of the available computer software and hardware. Thus, all the developed methods that are used for the effective seismic design of new or existing structures (e.g., linear or nonlinear static/dynamic analyses, provisions of modern codes for the design of reinforced concrete or steel members) are nowadays more applicable. In this framework, mainly in the last three decades, the research interest in the field of earthquake engineering has also turned to methods that are classified in the scientific field of machine learning. One of the most used methods of this field is based on the Artificial Neural Networks (ANNs). The ability of ANNs to efficiently perform multiparametric tasks, such as pattern recognition, classification and function approximation, has led to the idea of the use of them as computational tools in earthquake engineering.

The scope of this Special Issue is to attract research works dedicated to the application of ANNs for the improvement of the effectiveness of the seismic design of new structures or the seismic assessment and rehabilitation of existing ones. More specifically, papers from the following topics are welcome:

  • Pattern recognition in structural seismic analysis and design;
  • Seismic vulnerability assessment of existing structures;
  • Prediction of seismic damage of structural and non-structural members;
  • Structural material characterization and modeling;
  • Structural control;
  • Finite element mesh generation;
  • Structural system identification;
  • Structural condition assessment and monitoring.

Dr. Konstantinos Morfidis
Dr. Konstantinos Kostinakis
Guest Editors

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 2400 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

  • pattern recognition in structural seismic analysis and design
  • seismic vulnerability assessment of existing structures
  • prediction of seismic damage of structural and non-structural members
  • structural material characterization and modeling
  • structural control
  • finite element mesh generation
  • structural system identification
  • structural condition assessment and monitoring

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Published Papers (6 papers)

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Editorial

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4 pages, 169 KiB  
Editorial
Special Issue on Application of Artificial Neural Networks for Seismic Design and Assessment
by Konstantinos Morfidis and Konstantinos Kostinakis
Appl. Sci. 2022, 12(12), 6192; https://doi.org/10.3390/app12126192 - 17 Jun 2022
Cited by 2 | Viewed by 1245
Abstract
The application of methods and techniques of Machine Learning (ML) in many scientific fields has been increasing rapidly over recent decades [...] Full article
(This article belongs to the Special Issue Application of Artificial Neural Networks for Seismic Design and Assessment)

Research

Jump to: Editorial

22 pages, 1046 KiB  
Article
Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms
by Petros C. Lazaridis, Ioannis E. Kavvadias, Konstantinos Demertzis, Lazaros Iliadis and Lazaros K. Vasiliadis
Appl. Sci. 2022, 12(8), 3845; https://doi.org/10.3390/app12083845 - 11 Apr 2022
Cited by 31 | Viewed by 4612
Abstract
Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single [...] Read more.
Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application. Full article
(This article belongs to the Special Issue Application of Artificial Neural Networks for Seismic Design and Assessment)
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32 pages, 6775 KiB  
Article
Rapid Prediction of Seismic Incident Angle’s Influence on the Damage Level of RC Buildings Using Artificial Neural Networks
by Konstantinos Morfidis and Konstantinos Kostinakis
Appl. Sci. 2022, 12(3), 1055; https://doi.org/10.3390/app12031055 - 20 Jan 2022
Cited by 6 | Viewed by 1896
Abstract
The angle of seismic excitation is a significant factor in the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses, each using [...] Read more.
The angle of seismic excitation is a significant factor in the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses, each using different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerogram for the evaluation of seismic response; thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on multilayered feedforward perceptron neural networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class that arises from the application of the seismic motion along the buildings’ structural axes. To this end, the problem is expressed and solved as a pattern recognition problem. The ratios of seismic parameters’ values along the two horizontal seismic records’ components, as well as appropriately chosen structural parameters, were used as the inputs of the networks. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential. Full article
(This article belongs to the Special Issue Application of Artificial Neural Networks for Seismic Design and Assessment)
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19 pages, 1913 KiB  
Article
Training Data Selection for Machine Learning-Enhanced Monte Carlo Simulations in Structural Dynamics
by Denny Thaler, Leonard Elezaj, Franz Bamer and Bernd Markert
Appl. Sci. 2022, 12(2), 581; https://doi.org/10.3390/app12020581 - 7 Jan 2022
Cited by 10 | Viewed by 3056
Abstract
The evaluation of structural response constitutes a fundamental task in the design of ground-excited structures. In this context, the Monte Carlo simulation is a powerful tool to estimate the response statistics of nonlinear systems, which cannot be represented analytically. Unfortunately, the number of [...] Read more.
The evaluation of structural response constitutes a fundamental task in the design of ground-excited structures. In this context, the Monte Carlo simulation is a powerful tool to estimate the response statistics of nonlinear systems, which cannot be represented analytically. Unfortunately, the number of samples which is required for estimations with high confidence increases disproportionally to obtain a reliable estimation of low-probability events. As a consequence, the Monte Carlo simulation becomes a non-realizable task from a computational perspective. We show that the application of machine learning algorithms significantly lowers the computational burden of the Monte Carlo method. We use artificial neural networks to predict structural response behavior using supervised learning. However, one shortcoming of supervised learning is the inability of a sufficiently accurate prediction when extrapolating to data the neural network has not seen yet. In this paper, neural networks predict the response of structures subjected to non-stationary ground excitations. In doing so, we propose a novel selection process for the training data to provide the required samples to reliably predict rare events. We, finally, prove that the new strategy results in a significant improvement of the prediction of the response statistics in the tail end of the distribution. Full article
(This article belongs to the Special Issue Application of Artificial Neural Networks for Seismic Design and Assessment)
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14 pages, 2454 KiB  
Article
Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
by Xinzhe Yuan, Dustin Tanksley, Liujun Li, Haibin Zhang, Genda Chen and Donald Wunsch
Appl. Sci. 2021, 11(21), 9844; https://doi.org/10.3390/app11219844 - 21 Oct 2021
Cited by 14 | Viewed by 3079
Abstract
Contemporary deep learning approaches for post-earthquake damage assessments based on 2D convolutional neural networks (CNNs) require encoding of ground motion records to transform their inherent 1D time series to 2D images, thus requiring high computing time and resources. This study develops a 1D [...] Read more.
Contemporary deep learning approaches for post-earthquake damage assessments based on 2D convolutional neural networks (CNNs) require encoding of ground motion records to transform their inherent 1D time series to 2D images, thus requiring high computing time and resources. This study develops a 1D CNN model to avoid the costly 2D image encoding. The 1D CNN model is compared with a 2D CNN model with wavelet transform encoding and a feedforward neural network (FNN) model to evaluate prediction performance and computational efficiency. A case study of a benchmark reinforced concrete (r/c) building indicated that the 1D CNN model achieved a prediction accuracy of 81.0%, which was very close to the 81.6% prediction accuracy of the 2D CNN model and much higher than the 70.8% prediction accuracy of the FNN model. At the same time, the 1D CNN model reduced computing time by more than 90% and reduced resources used by more than 69%, as compared to the 2D CNN model. Therefore, the developed 1D CNN model is recommended for rapid and accurate resultant damage assessment after earthquakes. Full article
(This article belongs to the Special Issue Application of Artificial Neural Networks for Seismic Design and Assessment)
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33 pages, 1441 KiB  
Article
A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings
by Ehsan Harirchian, Vandana Kumari, Kirti Jadhav, Shahla Rasulzade, Tom Lahmer and Rohan Raj Das
Appl. Sci. 2021, 11(16), 7540; https://doi.org/10.3390/app11167540 - 17 Aug 2021
Cited by 39 | Viewed by 4035
Abstract
A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people [...] Read more.
A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings. Full article
(This article belongs to the Special Issue Application of Artificial Neural Networks for Seismic Design and Assessment)
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