Machine-learning for damage assessment of rubble stone masonry piers based on crack patterns-Reference-Cited by-同舟云学术

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Machine-learning for damage assessment of rubble stone masonry piers based on crack patterns

Published:2022-08 Issue: Volume:140 Page:104313
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Rezaie Amir,Godio Michele,Achanta Radhakrishna,Beyer Katrin

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference66 articles.

1. Assessment and analysis of damage in l’aquila historic city centre after 6th april 2009;D’Ayala;Bull. Earthq. Eng.,2011

2. Assessment and analysis of damage in l’aquila historic city centre after 6th april 2009;D’Ayala;Bull. Earthq. Eng.,2011

3. Perpetuate guidelines for seismic performance-based assessment of cultural heritage masonry structures;Lagomarsino;Bull. Earthq. Eng.,2015

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Assessment, repair, and retrofitting of masonry structures: A comprehensive review;Construction and Building Materials;2024-09

2. Full life-cycle vibration-based monitoring of a full-scale masonry arch bridge with increasing levels of damage;Engineering Structures;2024-09

3. A neural network‐based automated methodology to identify the crack causes in masonry structures;Computer-Aided Civil and Infrastructure Engineering;2024-07-22

4. Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls;Computer-Aided Civil and Infrastructure Engineering;2024-05-02

5. Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features;Structures;2024-04

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