Urban Ground Stability Evaluation via Traffic Noise Inversion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional inversion techniques for determining underground physical properties, such as S-wave velocity, face challenges with local minima convergence and decreased accuracy, particularly when initial values deviate from actual solutions, and probabilistic global optimization methods may compromise solution accuracy.
Innovation Solution
A system and method utilizing a surface wave dispersion curve inversion technique based on particle swarm optimization, which measures passive elastic wave signals generated by traffic noise, applies a frequency-phase velocity dispersion curve inversion, and verifies accuracy using a verification unit to derive S-wave velocity models, enhancing accuracy and overcoming local minima issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If local optimization technique is used for inversion, then computational efficiency is improved, but accuracy deteriorates when initial value deviates from actual solution
Solution Approach 1:
The inversion process is divided into two distinct stages: first, a global optimization algorithm is used to obtain an initial value that is close to the true solution, avoiding local minima; second, a local optimization algorithm is applied to refine this initial value and achieve high precision. This segmentation allows each algorithm to operate in its optimal performance range.
Solution Approach 2:
The global optimization algorithm performs preliminary action by searching the entire solution space to find a good initial value before the local optimization begins. This preliminary exploration ensures that the local optimization starts from a position that is already close to the global optimum, preventing convergence to incorrect local minima.
2Measurement precision
If global optimization technique is used for inversion, then accuracy is improved, but solution precision deteriorates due to probabilistic approximation
Solution Approach 1:
The inversion process is divided into two distinct stages: first, a global optimization algorithm is used to obtain an initial value that is close to the true solution, avoiding local minima; second, a local optimization algorithm is applied to refine this initial value and achieve high precision. This segmentation allows each algorithm to operate in its optimal performance range.
Solution Approach 2:
The output of the global optimization algorithm serves as feedback input for the local optimization algorithm. The initial value obtained through global search is fed into the local optimization process, which then refines it further. This feedback mechanism ensures that the strengths of both algorithms are combined to achieve both accuracy and precision.
3Device complexity
If conventional inversion methods are used, then device complexity is reduced, but reliability deteriorates due to convergence to local minima
Solution Approach 1:
The inversion process is divided into two distinct stages: first, a global optimization algorithm is used to obtain an initial value that is close to the true solution, avoiding local minima; second, a local optimization algorithm is applied to refine this initial value and achieve high precision. This segmentation allows each algorithm to operate in its optimal performance range.
Solution Approach 2:
The optimization approach changes parameters dynamically by switching from a global search strategy to a local refinement strategy. The algorithm adjusts its search behavior by changing the optimization scope and intensity at different stages, allowing it to both explore the solution space thoroughly and converge to precise solutions reliably.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for more accurate derivation of underground S-wave velocity, providing geological information essential for disaster response and safe urban infrastructure design, while improving upon the limitations of local and global optimization techniques.
Implementation Method 1
measuring a passive elastic wave signal generated by the traffic noise
Implementation Method 2
performing an inversion by applying a surface wave dispersion curve inversion technique to a frequency-phase velocity dispersion curve
Data Source
AI summary
An object of the present invention is to provide a system and a method for evaluating an urban ground stability using traffic noise, which derive a physical property (S wave velocity) according to a depth by performing an inversion for a surface wave dispersion curve generated by traffic vibration in order to more accurately derive an underground physical property value (S wave velocity).In order to achieve the object, a system for evaluating an urban ground stability using traffic noise according to the present invention includes: a signal measurement unit measuring a passive elastic wave signal generated by the traffic noise, and acquiring an elastic wave signal containing refracted waves using an artificial transmission source in an exploration area; and a server performing an inversion by applying a surface wave dispersion curve inversion technique to a frequency-phase velocity dispersion curve according to the passive elastic wave signal or the elastic wave signal containing the refracted wave.