Graph Neural Network–Based Spatiotemporal Structural Response Modeling in BuildingsSource: Journal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 002::page 04025006-1DOI: 10.1061/JCCEE5.CPENG-6229Publisher: American Society of Civil Engineers
Abstract: Modeling structural responses is vital in building structural health monitoring. This study proposed the graph network–based structure simulator (GNSS), a method employing graph neural networks, for spatiotemporal structural response modeling in buildings. GNSS considered both the spatial positions and connections of structural components and the temporal correlations of time-series structural data. The entire 6-story building was represented as a graph, with nodes representing mass and edges representing columns and beams. These nodes and edges captured time-series data about structural information, responses, and ground motion. GNSS included three components: encoder, processor, and decoder. Four GNSS model variations were explored (GNSS-NE, GNSS-N2E, GNSS-NUEU, and GNSS-Full), each investigating different feature integrations and graph network architectures. To assess GNSS’s predictive performance for structural responses (displacement and acceleration) under varying test conditions, three case studies were conducted: One-Step, Rollout, and Rollout&Calibration. Among the four model variations, GNSS-NE demonstrated superior performance in predicting both displacement and acceleration across all three case studies, except for displacement prediction in the Rollout scenario. Overall, GNSS models performed best in the One-Step case study, followed by Rollout&Calibration, with the lowest performance observed in the Rollout case study. These results highlight the significant potential of GNSS for extensive application in structural response modeling by effectively integrating spatial and temporal information.
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contributor author | Fangyu Liu | |
contributor author | Yongjia Xu | |
contributor author | Junlin Li | |
contributor author | Linbing Wang | |
date accessioned | 2025-04-20T10:25:35Z | |
date available | 2025-04-20T10:25:35Z | |
date copyright | 1/11/2025 12:00:00 AM | |
date issued | 2025 | |
identifier other | JCCEE5.CPENG-6229.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4304697 | |
description abstract | Modeling structural responses is vital in building structural health monitoring. This study proposed the graph network–based structure simulator (GNSS), a method employing graph neural networks, for spatiotemporal structural response modeling in buildings. GNSS considered both the spatial positions and connections of structural components and the temporal correlations of time-series structural data. The entire 6-story building was represented as a graph, with nodes representing mass and edges representing columns and beams. These nodes and edges captured time-series data about structural information, responses, and ground motion. GNSS included three components: encoder, processor, and decoder. Four GNSS model variations were explored (GNSS-NE, GNSS-N2E, GNSS-NUEU, and GNSS-Full), each investigating different feature integrations and graph network architectures. To assess GNSS’s predictive performance for structural responses (displacement and acceleration) under varying test conditions, three case studies were conducted: One-Step, Rollout, and Rollout&Calibration. Among the four model variations, GNSS-NE demonstrated superior performance in predicting both displacement and acceleration across all three case studies, except for displacement prediction in the Rollout scenario. Overall, GNSS models performed best in the One-Step case study, followed by Rollout&Calibration, with the lowest performance observed in the Rollout case study. These results highlight the significant potential of GNSS for extensive application in structural response modeling by effectively integrating spatial and temporal information. | |
publisher | American Society of Civil Engineers | |
title | Graph Neural Network–Based Spatiotemporal Structural Response Modeling in Buildings | |
type | Journal Article | |
journal volume | 39 | |
journal issue | 2 | |
journal title | Journal of Computing in Civil Engineering | |
identifier doi | 10.1061/JCCEE5.CPENG-6229 | |
journal fristpage | 04025006-1 | |
journal lastpage | 04025006-16 | |
page | 16 | |
tree | Journal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 002 | |
contenttype | Fulltext |