Shirin Jahanmiri, Ali Aalianvari, Maliheh Abbaszadeh
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Utilizing 21 parameters as input variables, intelligent AR models were formulated based on comprehensive training and testing patterns, incorporating geological features and the key machine parameters influencing AR. Quantitative evaluation of the models involved statistical indicators such as root mean square error (RMSE), coefficient of determination (<i>R</i><sup>2</sup>), and variance calculation. Comparative analysis based on RMSE, MAE, MAPE, VAF, and <i>R</i><sup>2</sup> superior gene expression function models showed that the gene expression algorithm with 1.41, 0.66, 6.33, 98.88, and 0.95 ahead of the nose is better than other approaches. 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引用次数: 0
摘要
在隧道工程项目中,隧道掘进机(TBM)的进尺率(AR)在评估其效率方面起着举足轻重的作用。本研究的重点是利用基因表达编程、时间序列分析、多元回归、人工神经网络、粒子聚集算法、遗传算法、自适应神经模糊推理系统和支持向量机等先进算法,开发隧道掘进机性能的精确预测模型。作为性能指标的 AR 成为预测模型的具体目标。从 Sar Pol Zahab、Bazi Daraz 输水隧道的一个掘进项目中整理出了一个包含 3597 个数据集的测试数据库。利用 21 个参数作为输入变量,在综合训练和测试模式的基础上,结合地质特征和影响 AR 的关键机器参数,建立了智能 AR 模型。模型的定量评估包括均方根误差(RMSE)、判定系数(R2)和方差计算等统计指标。基于 RMSE、MAE、MAPE、VAF 和 R2 的优势基因表达函数模型比较分析表明,基因表达算法的 1.41、0.66、6.33、98.88 和 0.95 超前性优于其他方法。这些结果凸显了基于基因表达编程模型的功效,表明它有可能产生一个新的功能方程,用于准确预测 TBM 性能。
A case study of tunnel boring machines advance rate prediction using meta-heuristic techniques
The advance rate (AR) of tunnel boring machines (TBMs) plays a pivotal role in evaluating their efficiency in tunnel engineering projects. This study focuses on the development of precise prediction models for TBM performance employing advanced algorithms, including gene expression programming, time series analysis, multivariate regression, artificial neural networks, particle aggregation algorithms, genetic algorithms, adaptive neural fuzzy inference systems, and support vector machines. The AR, serving as a performance metric, becomes the specific target for prediction models. A test database comprising 3597 datasets was curated from a tunneling project at the Sar Pol Zahab, Bazi Daraz water transfer tunnel. Utilizing 21 parameters as input variables, intelligent AR models were formulated based on comprehensive training and testing patterns, incorporating geological features and the key machine parameters influencing AR. Quantitative evaluation of the models involved statistical indicators such as root mean square error (RMSE), coefficient of determination (R2), and variance calculation. Comparative analysis based on RMSE, MAE, MAPE, VAF, and R2 superior gene expression function models showed that the gene expression algorithm with 1.41, 0.66, 6.33, 98.88, and 0.95 ahead of the nose is better than other approaches. These results underscore the efficacy of the gene expression programming-based model, suggesting its potential to yield a novel functional equation for accurate TBM performance prediction.
期刊介绍:
The Arabian Journal of Geosciences is the official journal of the Saudi Society for Geosciences and publishes peer-reviewed original and review articles on the entire range of Earth Science themes, focused on, but not limited to, those that have regional significance to the Middle East and the Euro-Mediterranean Zone.
Key topics therefore include; geology, hydrogeology, earth system science, petroleum sciences, geophysics, seismology and crustal structures, tectonics, sedimentology, palaeontology, metamorphic and igneous petrology, natural hazards, environmental sciences and sustainable development, geoarchaeology, geomorphology, paleo-environment studies, oceanography, atmospheric sciences, GIS and remote sensing, geodesy, mineralogy, volcanology, geochemistry and metallogenesis.