What is it about?
This paper presents a thorough reliability assessment of cavity foundation systems involving the generation of 272 datasets using Plaxis 2D automation. The parameters were systematically varied across feasible ranges, and Sobol-based sensitivity analysis identified the negligible influence of the soil modulus of elasticity (E) on subsequent reliability analyses. A robust 1D-CNN surrogate model was developed to predict the critical foundation responses by integrating Gaussian white noise to simulate real-world uncertainties. A log transformation with 1,000 bootstrap samples was chosen for resampling non-normally distributed data. This study employed a novel approach utilising 1D-CNN regressor models for bearing capacity (BC) prediction, achieving promising results with R2 values of 0.953 and 0.945 for BC in the training and testing phases, respectively. Bootstrapping resampling facilitates reliability analysis preparation and ensures robustness in handling complex data. Simulated noise varied with specific variance (p) from 0.01 to 0.5, allowing the examination of model efficacy under varying noise levels. Both the Monte Carlo Simulation (MCS) and first-order reliability method (FORM) were employed, revealing a reliability index (β) of 2.046 for FORM and 2.066 for MCS. This indicates a 0.976% increase in β and a 75% increase in the probability of failure transitioning from FORM to MCS, underscoring the model’s sensitivity to analytical methods.
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Why is it important?
the importance of foundation design in civil engineering, focusing on the need for stability and safety against material failure and settlement. The text highlights the complexity of designing foundations in urban areas, particularly when underground cavities and soft soils are involved. Advances in machine learning, specifically the 1D-CNN model, are crucial for improving the accuracy and efficiency of reliability analyses in geotechnical engineering. This model offers a significant advantage in processing high-dimensional data and providing precise results with lower computational costs, making it a valuable tool for modern foundation stability assessments.
Perspectives
the challenges and innovations in foundation design, emphasizing the importance of addressing settlement and stability issues, particularly in urban and excavation settings. It highlights the application of machine learning models, especially 1D convolutional neural networks (1D-CNNs), in geotechnical engineering for enhanced reliability analysis of foundation stability. The study integrates reliability methods like Monte Carlo simulations with 1D-CNN models, demonstrating their efficiency in handling high-dimensional data while reducing computational complexity. This approach advances foundation design by improving accuracy and addressing uncertainties in geotechnical parameters.
Dr Gobinath R
SR University, Warangal
Read the Original
This page is a summary of: One‐Dimensional‐Convolutional Neural Network (1D‐CNN) Based Reliability Analysis of Foundation Over Cavity Incorporating the Effect of Simulated Noise, Advances in Civil Engineering, January 2024, Wiley,
DOI: 10.1155/2024/9981433.
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