A hybrid machine learning model for predicting crater width formed by explosions of natural gas pipelines |
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| Institution: | 1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China;2. China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing 210096, China;1. School of Environmental and Chemical Engineering, Jiangsu Ocean University, Lianyungang, 222005, China;2. School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430070, China;3. Jiangsu Institute of Marine Resources Development, Jiangsu Ocean University, Lianyungang, 222005, China;1. School of Safety Science & Engineering, Xi''an University of Science and Technology, 58, Yanta Mid. Rd., Xi''an, 710054, Shaanxi, PR China;2. Department of Safety, Health, And Environmental Engineering, National Yunlin University of Science and Technology, 123, University Rd., Sec. 3, Douliou, Yunlin 64002, Taiwan, ROC;1. School of Safety Science and Engineering, Xi''an University of Science and Technology, 58, Yanta Mid. Rd., Xi''an, 710054, Shaanxi, PR China;2. Xi''an Key Laboratory of Urban Public Safety and Fire Rescue, 58, Yanta Mid. Rd., Xi''an, 710054, Shaanxi, PR China;3. Shaanxi Engineering Research Center for Industrial Process Safety & Emergency Rescue, 58, Yanta Mid. Rd., Xi''an, 710054, Shaanxi, PR China;4. Shaanxi Key Laboratory of Prevention and Control of Coal Fire, 58, Yanta Mid. Rd., Xi''an, 710054, Shaanxi, PR China;5. International Research Center for Renewable Energy, State Key Laboratory of Multiphase Flow in Power Engineering, Xi''an Jiaotong University, Xi''an, Shaanxi, 710049, PR China;6. Xi''an Xinzhu Fire&rescue Equipment Co., Ltd, Technology 1st Road 17, High-tech District, Xi''an, 710075, Shaanxi, PR China;1. College of Ocean and Safety Engineering, China University of Petroleum-Beijing, China;2. Department of Mechanics and Electrics Engineering, Hainan University, China |
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| Abstract: | Despite the existence of industry models for estimating the crater width formed by the explosion of natural gas pipelines, their applicability is still limited since the complex formation mechanisms. In this work, a novel hybrid model was developed to predict crater width formed by explosions of natural gas pipelines, using artificial neural networks (ANN) as the fundamental predictor. Based on the historical accident records, the proposed hybrid model was trained by the pipeline parameter, the operating condition, the installation parameter, and the crater width. A novel nature-inspired optimization algorithm, i.e., the Lévy-Weighted Quantum particle swarm optimization (LWQPSO) algorithm, was proposed to optimize the ANN model's parameters. Three machine learning models were developed for comparative reasons to predict the crater width. The use of precision and error analysis indicators assesses prediction performance. The results show that the proposed hybrid model (LWQPSO-ANN) has high prediction accuracy and stability, which outperforms QPSO-ANN-based benchmark hybrid models and the model without an optimizer (Support Vector Machine, SVM). The parameter sensitivities of the proposed algorithm, including the maximum number of iterations, population size and contraction-expansion coefficient, were determined. The proposed hybrid model is expected to support the quantitative risk assessment (QRA), Right-of-Way (ROW) definition and the inherently safer design of the underground parallel pipelines. |
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| Keywords: | Natural gas pipelines Crater width prediction Domino effect Artificial neural networks Quantum particle swarm optimization Machine-learning |
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