Prediction of soil classification in a metro line from seismic wave velocities using soft computing techniques

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hosein Chatrayi, Farnusch Hajizadeh, Behzad Shakouri
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引用次数: 0

Abstract

At a particular location on the ground, geotechnical measurements of soil properties are utilized to offer information for infrastructure design. Design uncertainty and dependability may increase when little point data is used. Geophysical techniques offer constant geographic information about the soil and are less time-consuming and intrusive. Geophysical data, however, is not expressed in terms of technical specifications. To enable the use of geophysical data in geotechnical designs, correlations between geotechnical and geophysical characteristics are required. The S- and P- seismic wave velocities are the main focus of the present geophysical technique research. Artificial neural network (ANN) models are developed using published data to predict seismic wave velocity and soil classification for seismic site effect evaluation. The results of ANN models using publicly available data demonstrate that seismic wave velocity has a moderate to high degree of accuracy in predicting soil classification. Regression is not as effective as artificial neural networks (ANN) in terms of overall performance. To confirm this, enclosed areas were evaluated to accurately predict soil classification and assess the performance of both ANN and regression models. The artificial neural network predicted the enclosed areas with much higher accuracy.

Abstract Image

利用软计算技术从地震波速度预测地铁线路中的土壤分类
在地面的特定位置,利用岩土工程测量土壤特性,为基础设施设计提供信息。如果使用的点数据很少,设计的不确定性和可靠性可能会增加。地球物理技术可提供有关土壤的恒定地理信息,耗时较短,侵入性较低。然而,地球物理数据并不是以技术规范的形式表达的。为了在岩土工程设计中使用地球物理数据,需要将岩土工程和地球物理特征联系起来。S 地震波速度和 P 地震波速度是目前地球物理技术研究的重点。利用已公布的数据开发了人工神经网络(ANN)模型,用于预测地震波速度和土壤分类,以评估地震场地效应。利用公开数据建立的人工神经网络模型的结果表明,地震波速度在预测土壤分类方面具有中等至高等程度的准确性。就整体性能而言,回归不如人工神经网络(ANN)有效。为了证实这一点,对封闭区域进行了评估,以准确预测土壤分类,并评估人工神经网络和回归模型的性能。人工神经网络预测封闭区域的准确性要高得多。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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