Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Quantum‐enhanced machine learning technique for rapid post‐earthquake assessment of building safety
Fast, accurate damage assessment of numerous buildings for large areas is vital for saving lives, enhancing decision‐making, and expediting recovery, thereby increasing urban resilience. The traditional methods, relying on expert mobilization, are slow and unsafe. Recent advances in machine learning (ML) have improved assessments; however, quantum‐enhanced ML (QML), a rapidly advancing field, offers greater advantages over classical ML (CML) for large‐scale data, enhancing the speed and accuracy of damage assessments. This study explores the viability of leveraging QML to evaluate the safety of reinforced concrete buildings after earthquakes, focusing on classification accuracy only. A QML algorithm is trained using simulation datasets and tested on real‐world damaged datasets, with its performance compared to various CML algorithms. The classification results demonstrate the potential of QML to revolutionize seismic damage assessments, offering a promising direction for future research and practical applications.
Quantum‐enhanced machine learning technique for rapid post‐earthquake assessment of building safety
Fast, accurate damage assessment of numerous buildings for large areas is vital for saving lives, enhancing decision‐making, and expediting recovery, thereby increasing urban resilience. The traditional methods, relying on expert mobilization, are slow and unsafe. Recent advances in machine learning (ML) have improved assessments; however, quantum‐enhanced ML (QML), a rapidly advancing field, offers greater advantages over classical ML (CML) for large‐scale data, enhancing the speed and accuracy of damage assessments. This study explores the viability of leveraging QML to evaluate the safety of reinforced concrete buildings after earthquakes, focusing on classification accuracy only. A QML algorithm is trained using simulation datasets and tested on real‐world damaged datasets, with its performance compared to various CML algorithms. The classification results demonstrate the potential of QML to revolutionize seismic damage assessments, offering a promising direction for future research and practical applications.
Quantum‐enhanced machine learning technique for rapid post‐earthquake assessment of building safety
Bhatta, Sanjeev (Autor:in) / Dang, Ji (Autor:in)
Computer‐Aided Civil and Infrastructure Engineering ; 39 ; 3188-3205
01.11.2024
18 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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