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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

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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

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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 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

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 (2 papers)

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Research

Open AccessArticle

Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation

Furkan Uysal

and

Rifat Sonmez

Buildings 2023, 13(3), 651; https://doi.org/10.3390/buildings13030651 - 28 Feb 2023

Viewed by 324

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 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)

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Open AccessArticle

Amplification in Mechanical Properties of a Lead Rubber Bearing for Various Exposure Times to Low Temperature

and

Buildings 2023, 13(2), 478; https://doi.org/10.3390/buildings13020478 - 10 Feb 2023

Viewed by 429

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 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)

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