Stability prediction of multi-material complex slopes based on self-attention convolutional neural networks

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL
Mansheng Lin, Xuedi Chen, Gongfa Chen, Zhiwei Zhao, David Bassir
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Abstract

This study proposes an integrated slope stability prediction model for various complex slope scenarios, including soil, rock, and rock-soil mixed situations. First, a small number of numerical slopes are constructed using the digital twin (DT) technique, and then these slope parameters are sorted and fine-tuned to build a database containing 19,666 soil, single/multiple sets of inclined joints, and rock-soil mixed slope scenarios. Second, the self-attention (SA) mechanism that can analyze the correlation of data features is connected to a classical convolutional neural network (CNN), forming a trained CNN-based SA model (CNN-SA) with 80% of the samples from the built database. The remaining 20% of the database and the stability of six actual slopes are then used for prediction. The performance of the CNN-SA is compared and evaluated. The results indicate that the DT technique is a reliable tool for providing the data to train the AI models, especially when the sample data is limited. As the complexity of the slopes increases, the prediction error of the models increases, and the CNN-based SA mechanism can effectively reduce these prediction errors compared to a classical CNN and other attention mechanisms.

Abstract Image

基于自注意卷积神经网络的多材料复杂斜坡稳定性预测
本研究针对各种复杂的边坡情况,包括土壤、岩石和岩土混合情况,提出了一种综合边坡稳定性预测模型。首先,利用数字孪生(DT)技术构建了少量数值边坡,然后对这些边坡参数进行分类和微调,建立了一个包含 19666 个土质、单/多组倾斜节理和岩土混合边坡场景的数据库。其次,将可分析数据特征相关性的自我关注(SA)机制与经典卷积神经网络(CNN)连接,利用所建数据库中 80% 的样本形成一个经过训练的基于 CNN 的 SA 模型(CNN-SA)。然后利用数据库中剩余的 20% 样本和六个实际斜坡的稳定性进行预测。对 CNN-SA 的性能进行了比较和评估。结果表明,DT 技术是为训练人工智能模型提供数据的可靠工具,尤其是在样本数据有限的情况下。随着斜坡复杂度的增加,模型的预测误差也随之增加,与经典 CNN 和其他注意力机制相比,基于 CNN 的 SA 机制可以有效减少这些预测误差。
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来源期刊
CiteScore
7.10
自引率
9.50%
发文量
189
审稿时长
3.8 months
期刊介绍: Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.
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