Concrete Defect Detection Using BiLSTM and Instantaneous Frequency
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Solution Overview
Problem
Current non-destructive testing methods for concrete structures face challenges in accurately detecting internal defects due to high computational costs, time requirements, and reliance on human expertise, particularly when dealing with long sequences and external noise interference.
Innovation Solution
An artificial intelligence-based system using a bidirectional LSTM model processes data with long sequences, incorporating a signal detection unit and data processing unit to generate frequency signals and instantaneous frequency data, which are used to determine defects through deep learning, minimizing computation time and improving classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional non-destructive testing methods (ultrasonic, magnetic, potential methods) are used to detect internal defects, then measurement coverage is improved, but measurement precision deteriorates due to external noise interference and difficulty in interpreting results
Solution Approach 1:
The patent segments the measurement process into multiple frequency components through Fourier transformation of the reflection signal. By analyzing specific frequency ranges corresponding to different defect depths and types, the system isolates defect-related signals from background noise, thereby improving measurement precision while maintaining broad coverage
Solution Approach 2:
The patent introduces an intermediary processing layer (signal processing unit performing Fourier transformation and frequency analysis) between the raw reflection signal and the defect determination. This intermediary extracts meaningful frequency characteristics that serve as a bridge, converting noisy time-domain signals into interpretable frequency-domain features for accurate defect detection
2Measurement precision
If core sampling method is used to accurately detect defect locations, then measurement precision is improved, but loss of time increases due to coring operations and repair work required
Solution Approach 1:
The patent replaces the mechanical core sampling process with a non-destructive signal-based detection system. By using impact excitation and reflection signal analysis, the system obtains precise defect location information without physical coring, eliminating the time-consuming mechanical extraction and subsequent repair operations while maintaining high measurement precision
Solution Approach 2:
The patent performs preliminary defect detection and localization before any physical intervention is needed. By accurately identifying defect positions through frequency analysis of reflection signals, the system provides advance information that eliminates the need for exploratory coring operations, thereby saving significant time in the overall inspection and repair process
3Reliability
If deep learning models process long sequence data for defect detection, then reliability is improved, but loss of time increases due to computational requirements
Solution Approach 1:
The patent extracts only the essential frequency characteristics from the reflection signal through Fourier transformation, isolating the most relevant features for defect detection. By taking out and focusing on specific frequency components rather than processing entire long-time sequences, the system reduces computational burden while maintaining high classification accuracy through targeted feature analysis
Solution Approach 2:
The patent transforms the time-domain reflection signal into the frequency domain through Fourier transformation, changing the parameter representation from time sequences to frequency spectra. This parameter change converts long temporal sequences into compact frequency distributions, reducing the dimensionality and computational complexity while preserving the essential information needed for reliable defect classification
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
The system enables rapid, accurate detection of internal defects in concrete members by leveraging bidirectional LSTM models and instantaneous frequency data, reducing training time and enhancing classification accuracy, allowing for real-time, wide-range non-destructive testing.
Implementation Method 1
detects a reflection signal by excitation applied to a concrete member
Implementation Method 2
generates a frequency signal from the impact echo signal, and extracts instantaneous frequency data from the frequency signal
Data Source
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AI summary
The present invention relates to a system for detecting internal defects in concrete members on the basis of artificial intelligence, wherein, when defects are determined by using deep learning in non-destructive inspection of concrete structures, a defect occurrence determination model is constructed by using a bidirectional LSTM model, and thus, the system is not only suitable for processing data having long sequences but can also improve the accuracy of classification through deep learning and minimize computation time.