[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126441-en":3,"doc-seo-126441-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126441,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Masonry Buildings Reconstruction Cost Post-Earthquake Analysis with Machine Learning - ECCOMAS Proceedia COMPDYN 2025","Machine learning (ML) methods enable advanced probabilistic, data-driven analysis that can strengthen earthquake engineering decisions by discovering patterns in complex datasets. This study evaluates ML classification models to predict post-earthquake reconstruction costs after the 2009 L’Aquila event in Abruzzi, using masonry building vulnerability indexes derived from AeDES forms and usability classes, complemented by seismic intensity measures (IMs). After exploratory data analysis, random forest experiments assess predictive value from vulnerability indexes alone. Results indicate potential for ML-assisted rapid cost simulations across reconstruction-cost classes, supporting cost-effective planning and seismic risk mitigation at regional scale.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMASONRY BUILDINGS RECONSTRUCTION COST POST-EARTHQUAKE ANALYSIS WITH MACHINE LEARNING  \nOriginal  \nMASONRY BUILDINGS RECONSTRUCTION COST POST-EARTHQUAKE ANALYSIS WITH MACHINE LEARNING / Di Battista, Nicola; Aloisio, Angelo; Rosso, Marco Martino; Marano, Giuseppe Carlo; Quaranta, Giuseppe; Demartino, Cristoforo; Fragiacomo, Massimo; Mannella, Antonio; Fico, Raffaello; D Alfonso, Tiziana. -2:(2025), pp. 4481-4490. ( 10th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering Rhodes Island, Greece 15-18 June 2025) [10 .7712/120125 .12750.24686] .  \nAvailability:  \nThis version is available at: 11583/3006338 since: 2026-01-08T11:22:41Z  \nPublisher:  \nECCOMAS Proceedia  \nPublished  \nDOI:10.7712/120125.12750.24686  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n21 February 2026  \nAvailable online [at](at www.eccomasproceedia.org)[ www.eccomasproceedia.org](at www.eccomasproceedia.org)[ ](at www.eccomasproceedia.org)Eccomas Proceedia COMPDYN (2025) 4481-4490  \nCOMPDYN 2025  \n10th ECCOMAS Thematic Conference on  \nComputational Methods in Structural Dynamics and Earthquake Engineering  \nM. Papadrakakis, M. Fragiadakis (eds.) Rhodes Island, Greece, 15-18 June 2025  \nMASONRY BUILDINGS RECONSTRUCTION COST POST-EARTHQUAKE ANALYSIS WITH MACHINE LEARNING  \nNicola Di Battista 1 , Angelo Alosio2 , Marco M. Rosso3 ,∗ , Giuseppe C. Marano3 , Giuseppe Quaranta4 , Cristoforo Demartino5 , Massimo Fragiacomo2 , Antonio Mannella6 ,  \nRaffaello Fico7 , Tiziana D’Alfonso 1  \n1 Department of Computer, Control, and Management Engineering, Sapienza University of Rome,  \nRome, Italy  \n2 Universit degli Studi dell’Aquila, via Giovanni Gronchi n. 18, L’Aquila 67100, Italy  \n3 Politecnico di Torino, Corso Duca Degli Abruzzi, 24, Turin 10128, Italy  \n4 Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Rome, Italy  \n5 Department of Architecture, Roma Tre University, Rome, Italy,  \n6 National Research Council of Italy, Construction Technologies Institute, CNR-ITC, L’Aquila Branch, L’Aquila Italy,  \n7 Special Office for Reconstruction for Municipalities of the Crater (USRC) -Abruzzo, Teramo, Italy,∗ Corresponding Author: marco.rosso@polito.it  \nAbstract. Machine learning (ML) methods represent advanced probabilistic data-driven techniques that can potentially empower earthquake engineering-related practices thanks to their ability to analyze hidden patterns buried in data. In this study, the authors examined the adoption of ML classification models for predicting reconstruction costs observed after L’Aquila 2009 earthquake event in the Abruzzi region based on masonry building vulnerability indexes and usability classes. The latter data mainly refers to the Rapid Post-Earthquake Damage Evaluation (AeDES) forms surveys, containing about 60 categorical features. Additionally, seismic intensity measures (IMs) have been incorporated into the database to encompass physics-based data characterizing the input demand. After an initial exploratory data analysis, the authors conducted some preliminary analysis on using the sole vulnerability index data for predictive purposes of the reconstruction costs using random forest algorithms. This approach could potentially benefit public administrations for long-term optimal resource earmarking on a regional scale. Indeed, preliminary findings from this study underscore the potential of using those ML assisted procedures for quick simulation scenarios based on various reconstruction cost classes, contributing to cost-effective planning and seismic risk mitigation strategies. However, in this preliminary work, the AeDES usability data have not been considered due to the purely pre dictive purpose conjecture carried on in this study. Therefore, fu","cbCaidEMMibtClpw","https://ap.wps.com/l/cbCaidEMMibtClpw","pdf",7623219,1,11,"English","en",105,"# Abstract\n# Introduction\n## Seismic risk assessment framework (PBEE)\n## Hazard, vulnerability, and exposure\n## Damage evaluation and predictive approaches","[{\"question\":\"What data sources are used to predict reconstruction costs in this study?\",\"answer\":\"The study uses masonry building vulnerability indexes and AeDES-based usability information, complemented by seismic intensity measures (IMs) representing the input demand.\"},{\"question\":\"Which ML method is tested for initial prediction using vulnerability index data?\",\"answer\":\"Random forest algorithms are applied to evaluate the predictive usefulness of vulnerability index data for reconstruction costs.\"},{\"question\":\"How do the authors position the results for public administrations?\",\"answer\":\"The findings suggest ML-assisted procedures could enable quick simulation scenarios across reconstruction-cost classes, supporting long-term, optimal resource allocation and seismic risk mitigation planning.\"}]","Masonry Buildings Reconstruction Cost Post-Earthquake Analysis with Machine Learning - 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