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Article
Construction & Building Technology
Rafael Ramirez Eudave et al.
Summary: This article proposes a novel approach to establish a vulnerability-oriented database linked to external services using open-source tools. The database is applied in a Geographical Information System (GIS) environment to survey the seismic vulnerability of masonry constructions based on an adapted version of the GNDT-II approach. Additionally, a customised Python-based software for reading, managing, and editing the database is presented, which includes calculations for vulnerability index and levels of damage. Furthermore, alternative methods for distributing the database, such as cloud-based distribution and the use of the Transactional Web Feature Service (WFS-T) protocol, are discussed.
Review
Multidisciplinary Sciences
Tirtharaj Dash et al.
Summary: This paper presents a survey on the inclusion of existing scientific knowledge when constructing models with neural networks. The inclusion of domain-knowledge is of special interest in constructing scientific assistants and other areas involving human-machine collaboration. The paper examines the inclusion of domain-knowledge through changes to input, loss-function, and architecture of deep networks, and describes techniques that have been shown to significantly improve the performance of deep neural networks.
SCIENTIFIC REPORTS
(2022)
Article
Geosciences, Multidisciplinary
Rafael Ramirez Eudave et al.
Summary: The seismic vulnerability assessment of large sets of constructions is meaningful for anticipating post-earthquake scenarios in the context of loss prevention and resilience. A dataset of information about the 2017 Earthquakes in Mexico is used to discuss the suitability of a pre-established seismic vulnerability index approach. Based on the results, it is plausible to confirm that this approach can be used in future seismic vulnerability assessment works in this region, provided that the limitations identified and discussed here are considered.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2022)
Article
Engineering, Geological
Sergio Ruggieri et al.
Summary: This paper presents an analytical-mechanical based procedure for estimating the seismic overall fragility of existing building portfolios in town compartments. It utilizes multi-source data to identify common typological features, conducts modeling and analysis on various ideal buildings, and computes fragility curves.
BULLETIN OF EARTHQUAKE ENGINEERING
(2022)
Review
Engineering, Multidisciplinary
Shashank Reddy Vadyala et al.
Summary: The recent development of machine learning and deep learning has provided opportunities in various sectors. However, applying ML directly to civil engineering can be challenging as simulated applications often fail in real-world tests. Physics-based ML models integrate data, partial differential equations, and mathematical models to address the issue of data shift and have important applications in civil engineering.
RESULTS IN ENGINEERING
(2022)
Article
Engineering, Geological
Annalisa Rosti et al.
Summary: This paper describes the derivation of an empirical fragility model for residential unreinforced masonry buildings based on Italian post-earthquake damage data. It also discusses the clustering strategy implemented to refine vulnerability classes and the specific procedure built up to determine the vulnerability composition of the exposed URM building stock. The empirically-derived model has been applied to the national seismic risk platform for assessing seismic risk in Italy.
BULLETIN OF EARTHQUAKE ENGINEERING
(2021)
Review
Archaeology
Mayank Mishra
Summary: This paper systematically reviews the application of machine learning techniques in assessing the health condition of heritage buildings, highlighting the importance of combining test data with ML to obtain more robust predictive models. Various applications of ML in predicting compressive strength, damage scenarios, seismic vulnerability, and other aspects of heritage buildings are discussed, along with the consideration of indirect factors for quantifying damage extent. Future research directions and challenges in using ML techniques for heritage buildings are also emphasized.
JOURNAL OF CULTURAL HERITAGE
(2021)
Article
Construction & Building Technology
Sergio Ruggieri et al.
Summary: The paper introduces a machine-learning based framework called VULMA for vulnerability analysis of existing buildings, utilizing photographs to provide seismic vulnerability indication. The framework includes four modules offering specific services and has been tested on actual buildings to assess its effectiveness, showing potential for advancements in vulnerability assessment and risk mitigation strategies.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Engineering, Geological
Rafael Ramirez Eudave et al.
Summary: This paper discusses the possibility of characterising an existing building based on a limited set of attributes to build a semantical, graphical, or conceptual model for analytical purposes. It also introduces the creation of the Mexican architectonic heritage catalogue and its application in recent seismic events for intervention decision making.
BULLETIN OF EARTHQUAKE ENGINEERING
(2021)
Article
Green & Sustainable Science & Technology
L. Gerardo F. Salazar et al.
Article
Green & Sustainable Science & Technology
Xiaoming Lei et al.
Article
Green & Sustainable Science & Technology
Jihye Han et al.
Article
Geochemistry & Geophysics
Valerie J. Sahakian et al.
BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA
(2018)
Article
Engineering, Geological
Yolanda Alberto et al.
SOILS AND FOUNDATIONS
(2018)
Article
Engineering, Civil
Tiago Miguel Ferreira et al.
EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION
(2017)
