Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.1
3.8
Journals
Buildings
Special Issues
Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches
share announcement

Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: closed (10 December 2023) | Viewed by 12262

Special Issue Editors

Dr. Shaghayegh Karimzadeh

E-Mail Website
Guest Editor
Institute for Sustainability in Structural Engineering (ISISE), Universidade of Minho, Campus de Azurém, Guimarães, Portugal
Interests: numerical modelling of ground motion records; probabilistic and deterministic seismic hazard analysis; nonlinear time history analysis; seismic vulnerability and risk analysis
Dr. Onur Kaplan

E-Mail Website
Guest Editor
College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
Interests: structural health monitoring; earthquake engineering; seismic safety; and vulnerability assessment; AI/data driven methods
Dr. Vasco Bernardo

E-Mail Website
Guest Editor
Institute for Sustainability in Structural Engineering (ISISE), Universidade of Minho, Campus de Azurém, Guimarães, Portugal
Interests: structural health monitoring; earthquake engineering; seismic safety; and vulnerability assessment; risk analysis; and reliability-based analysis

Special Issue Information

Dear Colleagues,

We are delighted to announce that Doctor Shaghayegh Karimzadeh will serve as the leading guest editor, in collaboration with Doctor Onur Kaplan and Doctor Vasco Bernardo as the co-editors, for a Special Issue of our journal that will be devoted to the application of Data-driven (DD), Machine Learning (ML) and Artificial Intelligence (AI) techniques to problems in civil and earthquake engineering. In recent years, DD/ML/AI approaches have proliferated, with the potential to drastically alter and enhance the role of data science in a variety of fields, including civil and earthquake engineering challenges. The Special Issue's emphasis is on applying more-advanced DD, ML and AI approaches to various civil engineering challenges and real-world problems, including those involving earthquake engineering, structural engineering, seismology, geotechnical and geophysical engineering. Moreover, this Special Issue aims to improve the transferability of research findings, the quality of data generation, sharing, and collection, the quality of the literature used to validate and compare models, and the process of identifying future work.

The following topics are of interest to this Special Issue but are not limited to:

  • DD/ML/AI-based approaches in structural engineering, seismology, geotechnics, and geophysics
  • Structural health monitoring applications
  • Vibration analysis on buildings
  • Numerical modelling of civil engineering structures
  • Data-driven approaches for seismic vulnerability and risk assessment
  • Risk mitigation and disaster management studies
  • Big data analysis for signal processing and microzonation studies
  • Ground motion modelling and simulation
  • Multi-hazard assessment, seismic safety and urban resilience studies
  • Performance-based design and assessment of civil engineering structures
  • Resilience-based design of civil engineering structures
  • Optimization of numerical approaches for seismic assessment
  • Seismic isolation

Dr. Shaghayegh Karimzadeh
Dr. Onur Kaplan
Dr. Vasco Bernardo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Buildings is an international peer-reviewed open access monthly 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 2600 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

  • machine learning approaches
  • artificial intelligence and data-driven
  • earthquake engineering
  • seismic safety assessment
  • risk analysis
  • ground motion modelling
  • multi-hazard assessment
  • structural health monitoring
  • resilience studies
  • performance-based design and assessment

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 6962 KiB  
Article
Utilising Artificial Neural Networks for Assessing Seismic Demands of Buckling Restrained Braces Due to Pulse-like Motions
by Amirhossein Mohammadi, Shaghayegh Karimzadeh, Saman Yaghmaei-Sabegh, Maryam Ranjbari and Paulo B. Lourenço
Buildings 2023, 13(10), 2542; https://doi.org/10.3390/buildings13102542 - 08 Oct 2023
Cited by 1 | Viewed by 751
Abstract
Buckling restrained brace frames (BRBFs) exhibit exceptional lateral stiffness, load-bearing capacity, and energy dissipation properties, rendering them a highly promising choice for regions susceptible to seismic activity. The precise and expeditious prediction of seismic demands on BRBFs is a crucial and challenging task. [...] Read more.
Buckling restrained brace frames (BRBFs) exhibit exceptional lateral stiffness, load-bearing capacity, and energy dissipation properties, rendering them a highly promising choice for regions susceptible to seismic activity. The precise and expeditious prediction of seismic demands on BRBFs is a crucial and challenging task. In this paper, the potential of artificial neural networks (ANNs) to predict the seismic demands of BRBFs is explored. The study presents the characteristics and modelling of prototype BRBFs with different numbers of stories and material properties, utilising the OpenSees software (Version 2.5.0) for numerical simulations. The seismic performance of the BRBFs is evaluated using 91 near-fault pulse-like ground motions, and the maximum inter-storey drift ratio (MIDR) and global drift ratio (GDR) are recorded as a measure of seismic demand. ANNs are then trained to predict the MIDR and GDR of the selected prototypes. The model’s performance is assessed by analysing the residuals and error metrics and then comparing the trend of the results with the real dataset. Feature selection is utilised to decrease the complexity of the problem, with spectral acceleration at the fundamental period (T) of the structure (Sa), peak ground acceleration (PGA), peak ground velocity (PGV), and T being the primary factors impacting seismic demand estimation. The findings demonstrate the effectiveness of the proposed ANN approach in accurately predicting the seismic demands of BRBFs. Full article
(This article belongs to the Special Issue Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches)
►▼ Show Figures

Figure 1

21 pages, 2999 KiB  
Article
The Sensitivity of Global Structural Parameters for Unreinforced Masonry Buildings Subjected to Simulated Ground Motions
by Ahmet Bahadir Koc, Murat Altug Erberik, Aysegul Askan and Shaghayegh Karimzadeh
Buildings 2023, 13(8), 2060; https://doi.org/10.3390/buildings13082060 - 13 Aug 2023
Cited by 1 | Viewed by 671
Abstract
This research performs a parametric study based on Equivalent Single Degree of Freedom (ESDOF) models for simplified seismic analysis of unreinforced masonry (URM) structures. This is a necessary action due to the fact that it is not affordable to model and analyze populations [...] Read more.
This research performs a parametric study based on Equivalent Single Degree of Freedom (ESDOF) models for simplified seismic analysis of unreinforced masonry (URM) structures. This is a necessary action due to the fact that it is not affordable to model and analyze populations of masonry buildings by using detailed continuum-based models during regional seismic damage and loss estimation studies. Hence, this study focuses on the sensitivity of major structural parameters of a selected idealized hysteretic model for URM buildings. The numerical models are subjected to region-specific simulated ground motion time histories generated using validated seismological parameters. The variations in dynamic analysis results are evaluated using statistical tools for major structural and seismological parameters. The results reveal that the strength factor is the most influential structural parameter, whereas magnitude and distance have a significant impact on the response of idealized URM models as seismological parameters. Furthermore, the specific seismic performance exhibiting limited ductility capacity and the narrow margin of safety between the initial state of inelastic behavior and the ultimate (collapse) state for URM buildings is verified by the statistical approaches employed in this study. Full article
(This article belongs to the Special Issue Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches)
►▼ Show Figures

Figure 1

15 pages, 2397 KiB  
Article
Data-Driven Model for Predicting the Compressive Strengths of GFRP-Confined Reinforced Concrete Columns
by Haolin Li, Dongdong Yang and Tianyu Hu
Buildings 2023, 13(5), 1309; https://doi.org/10.3390/buildings13051309 - 18 May 2023
Cited by 6 | Viewed by 1025
Abstract
This paper focuses on the compressive strength of Glass fiber reinforced polymer (GFRP)-confined reinforced concrete columns. Data from 114 sets of GFRP-confined reinforced concrete columns were collected to evaluate the researchers’ and proposed model. A data-driven machine learning model was used to model [...] Read more.
This paper focuses on the compressive strength of Glass fiber reinforced polymer (GFRP)-confined reinforced concrete columns. Data from 114 sets of GFRP-confined reinforced concrete columns were collected to evaluate the researchers’ and proposed model. A data-driven machine learning model was used to model the compressive strength of the GFRP-confined reinforced concrete columns and investigate the importance and sensitivity of the parameters affecting the compressive strength. The results show that the researchers’ model facilitates the study of the compressive strength of confined columns but suffers from a large coefficient of variation and too high or conservative estimation of compressive strength. The back propagation (BP) neural network has the best accuracy and robustness in predicting the compressive strength of the confined columns, with the coefficient of variation of only 14.22%, and the goodness of fit for both the training and testing sets above 0.9. The parameters that have an enormous influence on compressive strength are the concrete strength and FRP thickness, and all the parameters, except the fracture strain of FRP, are positively or inversely related to the compressive strength. Full article
(This article belongs to the Special Issue Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches)
►▼ Show Figures

Figure 1

24 pages, 7873 KiB  
Article
Continuous Monitoring of Elastic Modulus of Mortars Using a Single-Board Computer and Cost-Effective Components
by Thomas Russo, Renan Rocha Ribeiro, Amir Araghi, Rodrigo de Melo Lameiras, José Granja and Miguel Azenha
Buildings 2023, 13(5), 1117; https://doi.org/10.3390/buildings13051117 - 22 Apr 2023
Cited by 1 | Viewed by 1379
Abstract
The Elastic Modulus Measurement through Ambient Response Method (EMM-ARM) is designed to continuously monitor the elastic modulus of hardening construction materials such as concrete, cement paste, mortars, stabilized soils, and epoxy resin. In practice, a composite beam, made of the tested material in [...] Read more.
The Elastic Modulus Measurement through Ambient Response Method (EMM-ARM) is designed to continuously monitor the elastic modulus of hardening construction materials such as concrete, cement paste, mortars, stabilized soils, and epoxy resin. In practice, a composite beam, made of the tested material in its mould, is induced to vibration by means of environmental or controlled excitation, and its resonant frequency is identified. The material’s elastic modulus can then be calculated based on the vibration equation of structural systems. The traditional system to conduct EMM-ARM experiments is based on specialized equipment and on proprietary licensed software, which results in a considerable cost, as well as limited options for customization. The paper hereby presented proposes a delve into the development and validation of a cost-effective and open-source system that is able to conduct EMM-ARM experiments. By using a Raspberry Pi for the computing device and cost-effective electronic components, the cost of the system was one-twentieth of the traditional one, without compromising the measurement reliability. The composite beam’s excitation is generated, while the vibration response is recorded by the proposed system simultaneously, since the Raspberry Pi supports multiprocessing programming techniques. The flexibility earned by the exclusive use of open-source and cost-effective resources creates countless application possibilities for the proposed system. Full article
(This article belongs to the Special Issue Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches)
►▼ Show Figures

Figure 1

17 pages, 17012 KiB  
Article
Automatic Detection of Pedestrian Crosswalk with Faster R-CNN and YOLOv7
by Ömer Kaya, Muhammed Yasin Çodur and Enea Mustafaraj
Buildings 2023, 13(4), 1070; https://doi.org/10.3390/buildings13041070 - 18 Apr 2023
Cited by 13 | Viewed by 2913
Abstract
Autonomous vehicles have gained popularity in recent years, but they are still not compatible with other vulnerable components of the traffic system, including pedestrians, bicyclists, motorcyclists, and occupants of smaller vehicles such as passenger cars. This incompatibility leads to reduced system performance and [...] Read more.
Autonomous vehicles have gained popularity in recent years, but they are still not compatible with other vulnerable components of the traffic system, including pedestrians, bicyclists, motorcyclists, and occupants of smaller vehicles such as passenger cars. This incompatibility leads to reduced system performance and undermines traffic safety and comfort. To address this issue, the authors considered pedestrian crosswalks where vehicles, pedestrians, and micro-mobility vehicles collide at right angles in an urban road network. These road sections are areas where vulnerable people encounter vehicles perpendicularly. In order to prevent accidents in these areas, it is planned to introduce a warning system for vehicles and pedestrians. This procedure consists of multi-stage activities by sending warnings to drivers, disabled individuals, and pedestrians with phone addiction simultaneously. This collective autonomy is expected to reduce the number of accidents drastically. The aim of this paper is the automatic detection of a pedestrian crosswalk in an urban road network, designed from both pedestrian and vehicle perspectives. Faster R-CNN (R101-FPN and X101-FPN) and YOLOv7 network models were used in the analytical process of a dataset collected by the authors. Based on the detection performance comparison between both models, YOLOv7 accuracy was 98.6%, while the accuracy for Faster R-CNN was 98.29%. For the detection of different types of pedestrian crossings, YOLOv7 gave better prediction results than Faster R-CNN, although quite similar results were obtained. Full article
(This article belongs to the Special Issue Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches)
►▼ Show Figures

Figure 1

11 pages, 995 KiB  
Article
Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation
by Furkan Uysal and Rifat Sonmez
Buildings 2023, 13(3), 651; https://doi.org/10.3390/buildings13030651 - 28 Feb 2023
Cited by 3 | Viewed by 1313
Abstract
Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages [...] Read more.
Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages of construction projects. For this reason, numerous machine learning methods proposed in the literature that use different learning mechanisms. In recent years, the case-based reasoning (CBR) method has received particular attention in the literature for conceptual cost estimation of construction projects that use similarity-based learning principles. Despite the fact that CBR provides a powerful and practical alternative for conceptual cost estimation, one of the main criticisms about CBR is its low prediction performance when there is not a sufficient number of cases. This paper presents a bootstrap aggregated CBR method for achieving advancement in CBR research, particularly for conceptual cost estimation of construction projects when a limited number of training cases are available. The proposed learning method is designed so that CBR can learn from a diverse set of training data even when there are not a sufficient number of cases. The performance of the proposed bootstrap aggregated CBR method is evaluated using three data sets. The results revealed that the prediction performance of the new bootstrap aggregated CBR method is better than the prediction performance of the existing CBR method. Since the majority of conceptual cost estimates are made with a limited number of cases, the proposed method provides a contribution to CBR research and practice by improving the existing methods for conceptual cost estimating. Full article
(This article belongs to the Special Issue Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches)
►▼ Show Figures

Figure 1

18 pages, 4630 KiB  
Article
Amplification in Mechanical Properties of a Lead Rubber Bearing for Various Exposure Times to Low Temperature
by Cansu Yasar, Volkan Karuk, Onur Kaplan, Esengul Cavdar and Gokhan Ozdemir
Buildings 2023, 13(2), 478; https://doi.org/10.3390/buildings13020478 - 10 Feb 2023
Cited by 4 | Viewed by 1393
Abstract
In this paper, new formulations to predict the change in mechanical properties, namely, post-yield stiffness and characteristic strength of lead rubber bearings (LRBs) at low ambient temperatures, are proposed based on test results. Proposed formulations consider not only the effect of low temperature [...] Read more.
In this paper, new formulations to predict the change in mechanical properties, namely, post-yield stiffness and characteristic strength of lead rubber bearings (LRBs) at low ambient temperatures, are proposed based on test results. Proposed formulations consider not only the effect of low temperature but also the effect of exposure time to low temperature. Accordingly, a full-scale LRB was tested dynamically after being conditioned at temperatures of −20, −10, 0, and 20 °C for 3, 6, and 24 h. During the displacement-controlled cyclic tests, various levels of shear strain were applied to the isolator with loading frequencies of 0.1 Hz and 0.5 Hz. Then, force-displacement curves of LRB were recorded, and the corresponding amplifications in its hysteretic properties were noted. The accuracy of existing equations to estimate the amount of amplification in mechanical properties was evaluated through the experimental results. It was found that the existing formulas do not represent the effect of exposure time on LRB characteristics at low temperatures. On the other hand, the proposed equations result in highly accurate estimations of post-yield stiffness and characteristic strength of LRB at low temperatures for different exposure times. Full article
(This article belongs to the Special Issue Challenges in Civil and Earthquake Engineering Addressed by Data-Driven/AI Approaches)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop