Implementation of a Deep Learning Estimation Model for Identifying Induced Failure Hazard Areas in Construction Equipment
Abstract
Overturning accidents involving heavy construction vehicles may occur due to soft ground conditions caused by subsurface water accumulation or sinkholes. Although such accidents may appear unexpected during visual inspection, subsurface failure often precedes these events. Therefore, real-time assessment of ground stability is essential for improving operational safety.
Electrical Resistivity Tomography (ERT) is a widely used geophysical method for analyzing subsurface conditions through deep ground investigation. However, ERT data interpretation typically involves complex nonlinear inversion processes that are computationally intensive and highly dependent on initial parameter estimates.
In this study, an inference-based deep learning approach is proposed to efficiently identify areas susceptible to overturning accidents. The method converts data obtained using a Wenner array configuration into a high-resolution dipole–dipole configuration, thereby eliminating the need for traditional inversion procedures.
To evaluate the performance of the proposed framework, three deep learning models—Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—were trained and tested using regression-based loss functions. Among these models, the MLP demonstrated the best performance, achieving an average R² value of 0.9935 ± 0.0014 and a root mean square error (RMSE) of 3.44%.
The proposed approach leverages the inherent stability of the Wenner array and the high spatial resolution of the dipole–dipole configuration. By employing forward inference, the method significantly reduces the computational complexity associated with conventional inversion techniques. As a result, the efficiency and practical applicability of ERT-based ground inspection are substantially improved.
Overall, the proposed framework provides an effective tool for identifying hazardous ground conditions and assessing the susceptibility of construction sites to overturning accidents involving heavy vehicles.
Keywords: Deep learning; electrical resistivity tomography; ground stability assessment; construction equipment safety; overturning accident prediction; machine learning models; subsurface hazard detection.
Full Text:
PDFReferences
MILAZZO M.F., ANCIONE G., BRKIC V.S., and VALIS D. Investigation of crane operation safety by analyzing main accident causes. Risk, Reliability and Safety: Innovating Theory and Practice, 2017: 74–80. https://hdl.handle.net/11570/3121572
HAMID A.R.A. et al. Causes of crane accidents at construction sites in Malaysia. IOP Conference Series: Earth and Environmental Science, 2019, 220(1): 012028. https://doi.org/10.1088/1755-1315/220/1/012028
KIM S., and KANG C. Analysis of the complex causes of death accidents due to mobile cranes using a modified MEPS method. Sustainability, 2022, 14(5): 2948. https://doi.org/10.3390/su14052948
CHOI S.K., BACK S.H., AN J.B., and KWON T.H. Geotechnical investigation on causes and mitigation of ground subsidence during underground structure construction. Journal of Korean Tunnelling and Underground Space Association, 2016, 18: 143–154.
CHOI S.O. et al. Analysis of subsidence mechanism and development of evaluation program. Tunnelling and Underground Space, 2005, 15: 195–212.
BURGER H.R., SHEEHAN A.F., and JONES C.H. Introduction to Applied Geophysics: Exploring the Shallow Subsurface. Cambridge University Press, Cambridge, 2023. https://doi.org/10.1017/9781316567418
REYNOLDS J.M. An Introduction to Applied and Environmental Geophysics. John Wiley & Sons, Chichester, 1997.
LOKE M.H., and BARKER R.D. Rapid least-squares inversion of apparent resistivity pseudosections using a quasi-Newton method. Geophysical Prospecting, 1996, 44(1): 131–152. https://doi.org/10.1111/j.1365-2478.1996.tb00142.x
MENKE W. Geophysical Data Analysis: Discrete Inverse Theory. 3rd ed. Academic Press, Amsterdam, 2012. https://doi.org/10.1016/C2009-0-61848-4
ASTER R.C., BORCHERS B., and THURBER C.H. Parameter Estimation and Inverse Problems. 3rd ed. Elsevier, Amsterdam, 2018. https://doi.org/10.1016/C2016-0-04931-3
GUO Q., LIU B., WANG Y., and HE D. A Deep Learning Inversion Method for 3-D Electrical Resistivity Tomography Based on Neighborhood Feature Extraction. IEEE Sensors Journal, 2023, 23(16): 18550–18558. https://doi.org/10.1109/JSEN.2023.3293205
ALEARDI M., VINCIGUERRA A., and HOJAT A. A convolutional neural network approach to electrical resistivity tomography. Journal of Applied Geophysics, 2021, 193: 104434. https://doi.org/10.1016/j.jappgeo.2021.104434
PEREIRA J.L., and AZEVEDO L. Electrical resistivity tomography inversion combining deep variational autoencoder and stochastic adaptive sampling. Geophysics, 2025, 90(2): E41–E50. https://doi.org/10.1190/geo2023-0456.1
LOKE M.H. Electrical Imaging Surveys for Environmental and Engineering Studies: A Practical Guide to 2-D and 3-D Surveys. 1999. Available at: https://www.geotomosoft.com
JANG H. et al. Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System. Applied Sciences, 2024, 14: 9623. https://doi.org/10.3390/app14199623
JANG H., and CHOI S. A Study on an Analytical Model for Preventing Overturning of Mobile Cranes Using Deep Learning. Journal of Drive and Control, 2024, 21(4): 231–238.
RESIPY. ResIPy – Graphical User Interface for R2 family code, 2024. Available at: https://resipy.org
PYTHON SOFTWARE FOUNDATION. Python Language Reference (Version 3.10.11), 2024. Available at: https://www.python.org
HASTIE T., TIBSHIRANI R., and FRIEDMAN J. The Elements of Statistical Learning. 2nd ed. Springer, New York, 2009. https://doi.org/10.1007/978-0-387-84858-7
NIELSEN M. Neural Networks and Deep Learning. Determination Press, 2015. http://neuralnetworksanddeeplearning.com
GEMAN S., BIENENSTOCK E., and DOURSAT R. Neural networks and the bias/variance dilemma. Neural Computation, 1992, 4(1): 1–58. https://doi.org/10.1162/neco.1992.4.1.1
SEONG B. et al. Predicting the spray uniformity of pest control drone using multi-layer perceptron. Journal of Drive and Control, 2023, 20(3): 25–34.
JEONG H. et al. A Study of Weighing System to Apply into Hydraulic Excavator with CNN. Journal of Drive and Control, 2023, 20(4): 133–139.
HOWARD A.G. et al. Mobile Nets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint, 2017. https://arxiv.org/abs/1704.04861
ABADI M. et al. TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 2016: 265–283. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
LECUN Y., BENGIO Y., and HINTON G. Deep learning. Nature, 2015, 521: 436–444. https://doi.org/10.1038/nature14539.
Refbacks
- There are currently no refbacks.
