Field Validation of a Multi-Resolution ConvLSTM Framework for Retaining Wall Deformation Prediction
arXiv:2606.05556v1 Announce Type: new Abstract: This study presents a comprehensive field validation of a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) framework for predicting retaining wall deformation during staged excavation. The framework is trained on Gaussian noise-augmented numerical simulations and integrates ConvLSTM models operating at different temporal resolutions through a stacking ensemble strategy. The proposed framework is validated using field monitoring data from 34 inclinometers across 11 excavation sites in South Korea. Site-wise prediction performance i
The increasing maturity of AI/ML techniques like ConvLSTM and the availability of real-world monitoring data are enabling more robust field applications in civil engineering.
This development indicates a growing capability for AI to provide highly accurate predictive maintenance and risk assessment in critical infrastructure, reducing costs and improving safety.
The ability to accurately predict retaining wall deformation using AI shifts engineering from reactive maintenance to proactive, data-driven management of geotechnical risks.
- · Civil engineering firms
- · Infrastructure owners
- · AI/ML solution providers
- · Construction sector
- · Traditional structural monitoring companies
- · Risk assessment models lacking AI integration
Improved safety and reduced maintenance costs for critical infrastructure like retaining walls.
Broader adoption of AI for predictive analytics across various civil engineering and urban planning applications.
Enhanced resilience of urban environments against natural geotechnical hazards, potentially freeing up resources for other societal challenges.
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Read at arXiv cs.LG