[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121330-en":3,"doc-seo-121330-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"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},121330,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Rapid Safety Diagnosis of Regional Buildings Using Hybrid Quantum Machine Learning - ANCRiSST 2024","Seismic damage assessment for buildings often relies on experts deployed to earthquake-hit areas, making evaluations time-consuming, labor-intensive, and risky. Although machine learning and deep learning have been widely used to predict structural damage after earthquakes, quantum-enhanced machine learning (QML) remains less explored for damage assessment. This study evaluates the feasibility of applying QML for rapid safety classification of reinforced-concrete (RC) buildings after earthquakes, focusing on classification accuracy. QML performance on testing and validation datasets is compared with results from widely used classical machine learning (CML) algorithms.","ANCRiSST 2024  \n10-11 July 2024, Kyoto University, JAPAN  \nRapid Safety Diagnosis of Regional Buildings Using Hybrid Quantum Machine Learning  \nSanjeev Bhatta  \nSaitama University; [email:bhatta.s.070@ms.saitama-u.ac.jp](email:bhatta.s.070@ms.saitama-u.ac.jp)  \nJi Dang  \nSaitama University; [email:dangji@mail.saitama-u.ac.jp](email:dangji@mail.saitama-u.ac.jp)  \nABSTRACT  \nSeismic damage evaluations on buildings are often performed by government or local organizations mobilizing the experts to earthquake-affected areas, which can be time-consuming, labor-intensive, and risky. In recent years, numerous research has been conducted using machine learning and deep learning techniques, to assess the damage to building structures after an earthquake. However, the use of quantum-enhanced machine learning (QML) has a fewer work on damage assessment, which has advantages over classical machine learning (CML) algorithms in terms of larger datasets, computationally time and prediction accuracy as suggested by recent studies on various domain. Thus, this study currently examines the feasibility of using QML for rapid assessments of RC building safety after an earthquake in terms of classification accuracy. Furthermore, the performance of QML on testing and validation datasets is compared with the outcomes of widely used CML algorithms.   \nKEYWORDS: Quantum Machine Learning; Classical Machine learning; RC Buildings; Damage Assessment  \n1. INTRODUCTION  \nA 7.8 magnitude earthquake hit Nepal on 25 April 2015, claiming approximately 9000 lives, injuring over 23,000 people, and destroying over 500,000 houses, including 9000 schools. In addition, displacing approximately 2 million people (Adhikari, Mishra, and Raut 2016) and oneof the main reasons is the untimely damage assessment of building structures. Thus, an approach is required to quickly predict the safety of building structures immediately after an earthquake.  \nWith the increase in computation power, the use of machine learning (ML) techniques has increased significantly in predicting the seismic damage of building structures either utilizing simulation datasets or real-world damaged datasets. Zhang et al. (2023) and Bhatta and Dang (2023a) adopted the ML approach to classify the damage to reinforced concrete (RC) buildings into 5 damage categories utilizing the structural and ground motion features to train various ML algorithms. They achieved around 50% and 74.6% classification accuracy on the real-world damaged datasets, respectively. Likewise, Bhatta and Dang (2023b) further examined the feasibility of using the ML method for post-earthquake damage classification of buildings into five and three damage categories utilizing real-world damaged datasets collected after the 2015 Nepal earthquake. Stojadinovic et al. (2022) classified the damage into six damage categories using the damaged data collected after the 2010 Kraljevo Serbia earthquake.  \nRecently, the use of quantum-enhanced ML (QML) has increased in various domains (Bhatta and Dang, 2024a, such as classifying medical images (Sagingalieva et al., 2023), facial recognition (Peng and Li, 2007), speech recognition (Yang et al., 2023), classifying traffic signs (Kuros and Kryjak, 2022), classifying vehicle traffic photos under adversarial attack (Majumder  \net al., 2021) and categorizing organic and recyclable waste (Mogalapalli et al. 2022) . However, no research work has been reported utilizing the quantum-enhanced ML technique to classify the building’s safety after an earthquake. The QML in particular, can exploit superposition and entanglement, which are not present in classical computing environments, to provide high performance through parallelism amongst qubits (Bravyi et al. 2018) . This study aims to investigate the viability of using quantum-enhanced ML (QML) to predict the safety of RC buildings after an earthquake. Furthermore, this study aims to support concerned departments to know the most affected areas and to re","cbCaipWFkwTk4sny","https://ap.wps.com/l/cbCaipWFkwTk4sny","pdf",300672,1,6,"English","en",105,"# Introduction\n# Overall Methodology\n# Develop VQC and CML Algorithms","[{\"question\":\"Why is rapid building safety diagnosis important after an earthquake?\",\"answer\":\"Damage assessment must be completed immediately after earthquakes, but traditional expert-based evaluations are slow, labor-intensive, and risky for personnel deployed to affected areas.\"},{\"question\":\"What is the main goal of this study?\",\"answer\":\"To examine the feasibility of using quantum-enhanced machine learning to rapidly predict the safety of reinforced-concrete (RC) buildings after an earthquake, using classification accuracy as the key measure.\"},{\"question\":\"How are QML and classical ML methods compared in the work?\",\"answer\":\"Both are trained and evaluated using the same dataset split, where the variational quantum circuit is trained similarly to classical models, and performance is tested on a held-out testing set and a real-world damaged dataset.\"}]","Rapid Safety Diagnosis of Regional Buildings Using Hybrid Quantum Machine Learning - ANCRiSST 2024 | PDF",1785735093,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"rapid-safety-diagnosis-of-regional-buildings-using-hybrid-quantum-machine-learning-ancrisst-2024","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/rapid-safety-diagnosis-of-regional-buildings-using-hybrid-quantum-machine-learning-ancrisst-2024/121330/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is rapid building safety diagnosis important after an earthquake?","Question",{"text":75,"@type":76},"Damage assessment must be completed immediately after earthquakes, but traditional expert-based evaluations are slow, labor-intensive, and risky for personnel deployed to affected areas.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main goal of this study?",{"text":80,"@type":76},"To examine the feasibility of using quantum-enhanced machine learning to rapidly predict the safety of reinforced-concrete (RC) buildings after an earthquake, using classification accuracy as the key measure.",{"name":82,"@type":73,"acceptedAnswer":83},"How are QML and classical ML methods compared in the work?",{"text":84,"@type":76},"Both are trained and evaluated using the same dataset split, where the variational quantum circuit is trained similarly to classical models, and performance is tested on a held-out testing set and a real-world damaged dataset.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]