Geotechnical Risk Assessment using Graph Convolutional Networks and Hybrid LSTM-FEA Models in Mega Highway Projects

Mega highway projects are very sensitive to a number of geotechnical risks in the form of soil instability, seismic events, and environmental change, leading to costly failures. The current risk assessment methods are not capable of integrating the range of datasets—spatial data, temporal data, and real-time streams of data—and thus lack good levels of predictability, combined with a lack of response time. Thus, with a view to addressing these barriers, we introduce the new concept of Big Data Geotechnical Risk Assessment Model (BD-GRAM), which applies big data analytics and advanced machine learning algorithms in order to better predict and mitigate geotechnical risks for different scenarios. BD-GRAM combines various methods adapted to geotechnical data samples. The Graph Convolutional Networks (GCNs) are utilized for spatial and temporal data fusion, where complex spatial dependencies and temporal variations in soil properties, as well as samples of seismic data, are considered. GCNs, with the enhancement of attention mechanisms, have the ability to increase accuracy by as much as 20% compared with the conventional methods. A hybrid model by combining LSTMs and FEA, misleading synergistic use of physical laws, as well as temporal patterns of data pertaining to predictive accuracy, with an improvement of 25%. Near real-time processing on Apache Kafka & Apache Spark enables near real-time continuous monitoring of risk with alerting on. SHAP (Shapley Additive Explanations) ensures interpretability of the model outputs, as well as transparency of the factors driving the risk predictions. Lastly, the system is scalable using GPU-accelerated TensorFlow to Run Masses of datasets & samples. This fully integrated approach is optimized in this way to further enhance predictive accuracy and reduce false-positives and false-negatives, enhancing the speed and response of geotechnical risk assessment responses in real time. BD-GRAM represents an effective way to meet the early identification and mitigation of geotechnical challenges in a scalable data-driven manner for improved resilience and safety of large-scale infrastructures in any given highway scenario.