PSI - Issue 64

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000–000

www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 64 (2024) 791–798

SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Deep learning-based prediction model of temperature-induced deflection of a multi-span continuous box girder bridge: Case Study Lim Boon Xuan a , Gao Jing a, *, Gao Yanbin b a Department of Civil Engineering, Xiamen University, China b China Communications highway planning and Design Institute Co., Ltd., China Abstract The deflection of a bridge is a crucial factor in assessing its structural integrity and safety, serving as an indicator of the overall rigidity of the bridge. The deflections stemming from temperature variations may surpass those attributed to live loads. However, the monitoring data acquired from the sensors indicate a time-lag effect exists between temperature variations and resulting deflection. The time-lag effect poses challenges in precisely characterizing and modeling the temperature-induced deflection behavior. Therefore, this paper presents a deep learning-based prediction model to predict the temperature-induced deflection of multi-span continuous box girder bridges. The Long-Short Term Memory (LSTM) model was adapted in this paper leveraging deep learning techniques which capable to learn complex patterns and non-linear relations between temperature and temperature induced deflection data to predict the deflection of both span-1 and span-6 of bridge under ambient temperature and girder temperature variations respectively. To enhance the precision of the LSTM model, this paper proposed the Empirical Mode Decomposition (EMD) method to extract the temperature-induced deflection from the raw deflection data which induced by other live loads. Lastly, residual analysis was conducted to analyze the prediction outputs from the LSTM model with the actual measurements obtained from sensors. © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers a b © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers

Keywords: Box girder bridge; Temperature-induced deflection; Prediction; Deep learning; Empirical mode decomposition

* Corresponding author. E-mail address: gaojing@xmu.edu.cn

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 10.1016/j.prostr.2024.09.344