Tunnel Vulnerability Calculation Using Neural Networks
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Solution Overview
Problem
Current methods for calculating the structural vulnerability of tunnels are complex, time-consuming, and often result in inaccurate or incomplete vulnerability indices, making it difficult to predict and manage potential damage from geomorphological phenomena.
Innovation Solution
A computer-implemented method using an artificial trained neural network to process data sets related to tunnel structural features, geomorphological conditions, and potential phenomena, allowing for the calculation of accurate and reliable vulnerability indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional numerical simulations are used to model tunnel boundary conditions, then measurement precision and reliability of vulnerability indices are improved, but calculation time increases significantly making it incompatible with design timelines
Solution Approach 1:
The patent creates a simplified copy of the complex numerical simulation model by training an artificial neural network on simulation results. The neural network learns the input-output relationships from detailed simulations but executes predictions much faster, effectively copying the behavior of the complex model without its computational burden.
Solution Approach 2:
The patent replaces the mechanical numerical simulation process with an information-processing neural network system. Instead of repeatedly running complex finite element or finite difference simulations, the trained neural network processes inputs through learned patterns, substituting computational mechanics with intelligent information processing.
2Reliability
If traditional in situ surveys and monitoring are used to assess tunnel vulnerability, then reliability of structural condition assessment is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a universal vulnerability assessment system that handles multiple tunnel types, geomorphological conditions, and hazard scenarios through a single neural network model. The system processes diverse inputs (structural features, operational conditions, environmental factors) and provides comprehensive vulnerability assessments without requiring separate specialized systems for each scenario.
Solution Approach 2:
The neural network system performs self-assessment by automatically processing input data about tunnel characteristics and hazard conditions to generate vulnerability indices. The system serves itself by learning from training data and then independently evaluating new tunnel scenarios without requiring manual expert analysis for each case.
3Productivity
If simplified models are used for vulnerability calculation, then calculation speed is improved, but measurement precision and completeness of vulnerability information decrease
Solution Approach 1:
The patent performs preliminary action by training the neural network on comprehensive simulation data before actual vulnerability assessments. The training phase pre-computes the relationships between inputs and outputs using detailed models, so that subsequent predictions can be made quickly without sacrificing accuracy. The heavy computational work is done in advance during training.
Solution Approach 2:
The patent changes parameters by transforming the complex multi-dimensional simulation problem into a set of key input parameters that the neural network processes. The network learns to map these parameters to vulnerability indices, effectively changing the parameter space from continuous field simulations to discrete feature-based inputs that maintain precision while enabling fast computation.
Data Source
AI summary
The computer-implemented method for the calculation of the structural vulnerability of a tunnel comprises the following phases: - acquisition of at least one data set relating to structural characteristics of a tunnel under consideration and to the geomorphological characteristics of the construction site of the tunnel under consideration; - provision of a first data set as input to an artificial trained neural network; - processing of the data set by the trained neural network to obtain as output at least one vulnerability index of the tunnel under consideration, relating to the probability of the occurrence of damage to the tunnel under consideration itself depending on a geomorphological phenomenon; - mapping of the tunnel under consideration depending on the vulnerability index.