TY  - SER
AU  - Peerawut Watsaratiyanont
AU  - Krishna,Chaitanya
AU  - Pennung Warnitchai,
AU  - Panon Latcharote,
AU  - Raktipong Sahamitmongkol,
ED  - Royal Thai Government Fellowship,
TI  - Data-driven numerical model updating of reinforced concrete structures using artificial neural networks
T2  - Thesis
PY  - 2025///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Reinforced concrete
KW  - Concrete construction
KW  - Artificial Intelligence
N1  - 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
N2  - 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
UR  - http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B23418
ER  -