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Advanced Application of Big Data, Artificial Intelligence, Deep Learning, and...
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Advanced Application of Big Data, Artificial Intelligence, Deep Learning, and Machine Learning in Earthquake Engineering

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

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 4202

Special Issue Editors

Dr. Antonio Mannella

E-Mail Website
Guest Editor
Construction Technologies Institute (ITC), National Research Council of Italy (CNR), 67100 L’Aquila, Italy
Interests: earthquakes engineering; building structural analysis; geophysical image processing; post-earthquake reconstruction; conservation of historical buildings; sustainable regeneration
Dr. Agostino Forestiero

E-Mail Website
Guest Editor
Institute for High-Performance Computing and Networking, National Research Council of Italy, 87036 Cosenza, Italy
Interests: artificial intelligence; personalized medicine; patient-centric care; healthcare IoT; biomedical data integration; precision health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The availability of high computing capabilities, the use of artificial intelligence algorithms based on deep learning, machine learning, and the availability of increasingly large databases is opening up new possibilities in the progress of Earthquake Engineering.

Therefore, this Special Issue is intended to present new contributions and results, including experimental results, from the use of such technologies applied to Earthquake Engineering.

Relevant areas include, but are not limited to: high-resolution computational models concerning the behavior of structures or individual building elements subjected to seismic action; advanced prediction models of damage to individual structural and nonstructural elements and their interactions; artificial intelligence applications for automatic recognition of damage resulting from seismic events, both on individual structures and on a large scale, from drones, satellites or other sources; the use of sensor networks, digital survey and other data sources; the simulations of seismic wave propagation; the processing of data retrieved from social networks; and the definition of damage scenarios and the earthquake early warning system.

Dr. Antonio Mannella
Dr. Agostino Forestiero
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. 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

  • high-performance computing
  • artificial intelligence
  • machine learning and deep learning
  • big data applications and algorithms
  • modeling and simulation
  • seismic response of structures
  • earthquake protection system
  • digital twin
  • earthquake damage
  • seismic wave
  • post-earthquake damage assessment
  • earthquake early warning
  • damage scenarios

Published Papers (2 papers)

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Research

15 pages, 12103 KiB  
Article
Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
by Miguel Ureña-Pliego, Rubén Martínez-Marín, Beatriz González-Rodrigo and Miguel Marchamalo-Sacristán
Appl. Sci. 2023, 13(8), 5037; https://doi.org/10.3390/app13085037 - 17 Apr 2023
Cited by 2 | Viewed by 2074
Abstract
Artificial intelligence (AI) is delivering major advances in the construction engineering sector in this era of building information modelling, applying data collection techniques based on urban image analysis. In this study, building heights were calculated from street-view imagery based on a semantic segmentation [...] Read more.
Artificial intelligence (AI) is delivering major advances in the construction engineering sector in this era of building information modelling, applying data collection techniques based on urban image analysis. In this study, building heights were calculated from street-view imagery based on a semantic segmentation machine learning model. The model has a fully convolutional architecture and is based on the HRNet encoder and ResNexts depth separable convolutions, achieving fast runtime and state-of-the-art results on standard semantic segmentation tasks. Average building heights on a pilot German street were satisfactorily estimated with a maximum error of 3 m. Further research alternatives are discussed, as well as the difficulties of obtaining valuable training data to apply these models in countries with no training datasets and different urban conditions. This line of research contributes to the characterisation of buildings and the estimation of attributes essential for the assessment of seismic risk using automatically processed street-view imagery. Full article
(This article belongs to the Special Issue Advanced Application of Big Data, Artificial Intelligence, Deep Learning, and Machine Learning in Earthquake Engineering)
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19 pages, 6629 KiB  
Article
Development of a Control Algorithm for a Semi-Active Mid-Story Isolation System Using Reinforcement Learning
by Hyun-Su Kim and Uksun Kim
Appl. Sci. 2023, 13(4), 2053; https://doi.org/10.3390/app13042053 - 04 Feb 2023
Cited by 1 | Viewed by 1559
Abstract
The semi-active control system is widely used to reduce the seismic response of building structures. Its control performance mainly depends on the applied control algorithms. Various semi-active control algorithms have been developed to date. Recently, machine learning has been applied to various engineering [...] Read more.
The semi-active control system is widely used to reduce the seismic response of building structures. Its control performance mainly depends on the applied control algorithms. Various semi-active control algorithms have been developed to date. Recently, machine learning has been applied to various engineering fields and provided successful results. Because reinforcement learning (RL) has shown good performance for real-time decision-making problems, structural control engineers have become interested in RL. In this study, RL was applied to the development of a semi-active control algorithm. Among various RL methods, a Deep Q-network (DQN) was selected because of its successful application to many control problems. A sample building structure was constructed by using a semi-active mid-story isolation system (SMIS) with a magnetorheological damper. Artificial ground motions were generated for numerical simulation. In this study, the sample building structure and seismic excitation were used to make the RL environment. The reward of RL was designed to reduce the peak story drift and the isolation story drift. Skyhook and groundhook control algorithms were applied for comparative study. Based on numerical results, this paper shows that the proposed control algorithm can effectively reduce the seismic responses of building structures with a SMIS. Full article
(This article belongs to the Special Issue Advanced Application of Big Data, Artificial Intelligence, Deep Learning, and Machine Learning in Earthquake Engineering)
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