Peerawut Watsaratiyanont creator Krishna, Chaitanya Chairperson Pennung Warnitchai Examination Committee Panon Latcharote Examination Committee Raktipong Sahamitmongkol Examination Committee Royal Thai Government Fellowship Scholarship Donor text series technical report th Pathum Thani, Thailand Asian Institute of Technology 2025 continuing eng

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79 leaves : ill.+ 1 online resource This research develops and evaluates a data-driven model updating framework for reinforced concrete (RC) building structures using Artificial Neural Networks (ANN) and the 3D Applied Element Method (AEM). The proposed method aims to improve the accuracy of gradient-based model updating by providing more realistic initial estimates of material properties based on measured modal parameters.Databases were generated by varying Young{u2019}s modulus in 3D AEM models and recording corresponding modal responses. ANN models trained on these databases achieved high predictive accuracy in low- and moderate-resolution configurations. Verification on a controlled numerical model demonstrated improved convergence and robustness compared to conventional random-initial-guess approaches. Validation on two experimental case studies showed that the method produces updated models with plausible material properties and improved agreement with measured modal data.The results confirm that the proposed framework effectively integrates data-driven prediction with numerical updating, providing a practical and efficient approach for RC building model updating. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Structural Engineering Thesis (M. Eng.) - Asian Institute of Technology, 2025 Reinforced concrete Concrete construction Artificial Intelligence Asian Institute of Technology. http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B23418 http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B23418 260202 20260818085742.0