Research on Multispectral Concrete-Mud Boundary Detection Technology
HAO Xiang-wei1, XING Jian1, MA Jia-qiang1*, LIANG Jian-jun2, ZONG Yun-cui3
1. Northeast Forestry University, Harbin 150040, China
2. Heilongjiang Construction Investment Group Co., Ltd., Harbin 150040, China
3. Heilongjiang Provincial Highway Construction Center, Harbin 150040, China
Abstract：During construction, it is necessary to determine the classification of concrete and mud within the pouring area. Currently, most projects use manual detection methods. To ensure engineering quality, the height of poured concrete piles often far exceeds the design value, leading to significant waste of concrete. To address this issue, an automatic mud boundary detection technology based on multispectral imaging is proposed.First, a spectrometer was used to analyze the visible light reflection spectra of 11 mixtures of concrete and mud at different ratios. The results show a functional relationship between the mixing ratio coefficient K and spectral reflectance. The calculation formula for reflectance was used to determine that reflected light intensity can be used as a substitute forreflectance. Based on this, a monitoring system was designed using the AS7341 spectral chip, which consists of 8 visible light spectral bands, an STM32 microcontroller, and a JDY-31 Bluetooth communication module, all enclosed in a transparent housing. Multispectral reflected light intensity data were collected for the 11 concrete-mud mixtures at different ratios, forming a dataset of reflected light intensity values for mixtures of loess and black soil with concrete at varying proportions. It was found that the reflected light intensity values fluctuated over time. Therefore, a mud-concrete boundary prediction algorithm based on a Convolutional Long Short-Term Memory Network with Attention Mechanism (CNN-LSTM-Attention) was proposed. The CNN network extracts key features from the input reflected light intensity data of 8 channels to capture more local features and improve subsequent prediction accuracy. The LSTM layer adds interfaces and reverse gates to the CNN layer to enable backpropagation, avoiding gradient disappearance and explosion. Finally, the Attention function focuses on more critical spectral information among the input reflectance values at multiple wavelengths, thereby reducing or ignoring other spectral information to address the problem of information overload and improve efficiency.Simulation results show that the algorithm achieves a precision of 0.952, a recall of 0.944, and an F1 score of 0.938. Compared with other algorithms, it demonstrates higher accuracy and stability, meeting national standards for construction sites. This method eliminates the need for manual measurement. It enables real-time remote control of spectral data collection via a host computer, providing direct detection results for the boundary between concrete and mud.
[1] Ho H J, Iizuka A, Shibata E. Journal of Cleaner Production, 2021, 284: 124785.
[2] Nilimaa J. Developments in the Built Environment, 2023, 15: 100177.
[3] Günçan N F. Cement and Concrete Research, 1995, 25(7): 1385.
[4] Guan L, Tong Y, Li J, et al. Optik, 2018, 164: 277.
[5] BAI Tie-cheng, WANG Ya-ming, ZHANG Nan-nan(白铁成，王亚明，张楠楠，等). Spectroscopy and Spectral Analysis（光谱学与光谱分析），2017，37(11): 3419.
[6] Chung W H, Gu Y H, Yoo S J. Energy, 2022, 246: 123350.
[7] Borré A, Seman L O, Camponogara E, et al. Sensors, 2023, 23(9): 4512.
[8] Zhang Y, Zheng J, Jiang Y, et al. Chinese Journal of Electronics, 2019, 28(1): 120.
[9] Bec K B, Grabska J, Huck C W. Chemistry-A European Journal, 2021, 27(5): 1514.
[10] Liu Q, Andersen L V, Wu M. Cement and Concrete Composite, 2024, 148: 105431.
[11] Sherstinsky A. Physica D: Nonlinear Phenomena, 2020, 404: 132306.
[12] Cai R C, Zhu B J, Liu W Y, et al. An CNN-LSTM Attention Approach to Understanding User Query Intent from Online Health Communities[C]. 2017 17th IEEE International Conference on Data Mining (ICDMW), 2017: 430.
[13] Cui B, Liu M, Li S, et al. Atmospheric Pollution Research, 2023, 14(9): 101833.
[14] Li S, Simonian A, Chin B A. The Electrochemical Society Interface, 2010, 19(4): 41.
[15] Erdle K, Mistele B, Schmidhalter U. Field Crops Research, 2012, 124(1): 74.
[16] Huang L, Hu L, Yu M, et al. Compact Data Acqui-Sition System for Micro Spectrometer[C]. in Eleventh International Conference on Information Optics and Photonics (CIOP 2019). SPIE, 2019, 11209: 755.
