•AI/ Engineering

AI-Based Slope Stability Risk Prediction System

Development of an AI-based system for assessing slope stability and risk at high-voltage transmission tower sites, using machine learning to analyze engineering data and support infrastructure safety decision-making.

Engineering Risk Prediction Accuracy
Average ≈ 90% across AI models

Challenge

Identify problems and needs

Solution

Design and develop

Results

Deliver value

Engineering Risk Prediction Accuracy
Average ≈ 90% across AI models
Validated Accuracy
Reduced computation time compared to traditional numerical analysis
Validated Accuracy
Consistent performance validated against PLAXIS and field data
Slope stability
Support for four slope stability conditions

The Challenge

High-voltage transmission towers are often located on slopes with potential failure risks. Traditional slope stability analysis is time-consuming and lacks flexibility in handling complex loading scenarios.The organization required a system capable of delivering fast, accurate, and explainable engineering risk assessments. S1lope.png

Our Solution

ThinkSpace Technology engineered a sophisticated Machine Learning Dynamic Correlation Matrix module to calculate_ Factor of Safety_ assessments by synthesizing field surveys, laboratory testing, and PLAXIS 2D numerical simulations. The system incorporates specialized ML models for diverse load configurations, an automated Validation & QA layer, and an Active Retraining mechanism. The entire suite is deployed within a secure, Dockerized local environment to maintain the highest standards of data confidentiality and system integrity Slope2.png

Technologies Used

Machine Learning (XGBoost, LightGBM, CatBoost)PLAXIS 2D Numerical SimulationDynamic Correlation MatrixPythonDockerized Local Secure EnvironmentFastAPI Backend

The Results

Engineering Risk Prediction Accuracy
Average ≈ 90% across AI models
Validated Accuracy
Reduced computation time compared to traditional numerical analysis
Validated Accuracy
Consistent performance validated against PLAXIS and field data
Slope stability
Support for four slope stability conditions

Key Achievements

Accurate slope stability risk prediction using AI models trained on PLAXIS simulation data
Four specialized machine learning models supporting multiple load configurations (CU, UC, UU, CC)
Improved decision support for high-voltage transmission tower risk management
Explainable risk assessment through feature importance and correlation analysis
Secure offline deployment using Dockerized Local Secure Environment

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