Journal ArticleDOI
ML-EHSAPP: a prototype for machine learning-based earthquake hazard safety assessment of structures by using a smartphone app
TLDR
The recent devastating earthquakes have caused severe physical, social, and financial damage worldwide and indicate that many existing buildings, especially in developing countries, are not designe...Abstract:
The recent devastating earthquakes have caused severe physical, social, and financial damage worldwide and indicate that many existing buildings, especially in developing countries, are not designe...read more
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A review on application of soft computing techniques for the rapid visual safety evaluation and damage classification of existing buildings
Ehsan Harirchian,Seyed Ehsan Aghakouchaki Hosseini,Kirti Jadhav,Vandana Kumari,Shahla Rasulzade,Ercan Işik,Muhamad Wasif,Tom Lahmer +7 more
TL;DR: There are structures still in service with a high seismic vulnerability, which proposes an urgent need for a screening system’s damageability grading system, and the necessity of developing a rapid, reliable, and computationally easy method of seismic vulnerability assessment, more commonly known as RVS.
Journal ArticleDOI
Machine-learning based vulnerability analysis of existing buildings
TL;DR: In this article, a machine learning-based framework, named VULMA (VULnerability analysis using machine learning), is proposed for vulnerability analysis of existing buildings in order to provide an indication of the seismic vulnerability by exploiting available photographs.
Journal ArticleDOI
A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings
TL;DR: In this paper , an Artificial Neural Network (ANN)-based model was developed to predict risk priorities for reinforced-concrete (RC) buildings that constitute a large part of the existing building stock.
Journal ArticleDOI
A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings
TL;DR: Five different Machine Learning techniques in vulnerability prediction applications have been investigated and it is illustrated that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.
Journal ArticleDOI
A Two-Stage Seismic Damage Assessment Method for Small, Dense, and Imbalanced Buildings in Remote Sensing Images
TL;DR: In this paper , a machine-learning-derived two-stage method for post-earthquake building location and damage assessment considering the data characteristics of satellite remote sensing (SRS) optical images with dense distribution, small size, and imbalanced numbers was developed.
References
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Receiver Operating Characteristic Curve in Diagnostic Test Assessment
TL;DR: The salient features of the ROC curve are discussed, as well as the area under the R OC curve, and its utility in comparing two different tests or predictor variables of interest are discussed.
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On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
TL;DR: It is demonstrated that a low variance is at least as important, as a non-negligible variance introduces the potential for over-fitting in model selection as well as in training the model, and some common performance evaluation practices are susceptible to a form of selection bias as a result of this form of over- fitting and hence are unreliable.
Related Papers (5)
Earthquake Hazard Safety Assessment of Buildings via Smartphone App: A Comparative Study
Ehsan Harirchian,Tom Lahmer +1 more