水泥处理粘土强度发展的统计与预测分析

IF 2.2 4区 工程技术 Q3 ENGINEERING, GEOLOGICAL Environmental geotechnics Pub Date : 2023-06-07 DOI:10.3390/geotechnics3020026
A. Abdallah, G. Russo, O. Cuisinier
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引用次数: 1

摘要

水泥稳定土的力学效率主要受粘结剂用量、压实土状态和养护条件等参数的控制。处理后土壤的强度是几个因素复杂组合的结果,这些因素决定了水泥水化过程中涉及的物理化学过程,这些因素很难监测。本研究的目的是确定控制水泥处理土壤粘结的相关参数,并提出使用这些参数作为输入的强度演变预测模型。为此,提出了一项广泛的测试计划,以评估在密封养护条件下0,7,28和90天的初始含水量(11-18%)和干密度(1.6-1.87 Mg/m3)以及水泥用量(3%和6%)的影响。测定了不同养护时间后的含水率变化、总吸力和抗压强度。实验结果首先在参数空间内进行讨论,然后通过主成分分析克服了参数相互依赖所带来的复杂性。PCA显示,水泥用量解释了数据集方差的40%,其余60%与初始状态和养护时间的组合有关。最后,建立了基于人工神经网络的预测模型并进行了测试。预测结果非常好,训练数据的R2为+0.99,测试数据的R2为+0.93。这些结果应该通过扩展数据集来改进,以包括不同的土壤和额外的压实条件。
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Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil
The mechanical efficiency of soil stabilization with cement is mainly controlled by various parameters, namely, the amount of binder, the compaction soil state and the curing conditions. The strength of the treated soil is the result of a complex combination of several factors that condition the physicochemical processes involved in cement hydration, which are difficult to monitor. The objective of this study is to identify the relevant parameters governing the bonding in cement-treated soil and suggest a predictive model for strength evolution using these parameters as input. To this purpose, an extensive testing program is presented to assess the impact of the initial water content (11–18%) and dry density (1.6–1.87 Mg/m3) as well as cement dosage (3 and 6%) in sealed curing conditions for 0, 7, 28 and 90 days. The water content variation, the total suction and the compressive strength were measured after different curing durations. The experimental results are first discussed in the parameters’ space, and then through a principal components analysis to overcome the complexity due to the parameters’ interdependency. The PCA revealed that the cement dosage explained 40% of the dataset variance, the remaining 60% being related to a combination of the initial state and curing time. Finally, a predictive model based on an artificial neural network was developed and tested. The predicted results were excellent, with an R2 of +0.99 with the training data and +0.93 with the testing data. These results should be improved by extending the dataset to include different soils and additional compaction conditions.
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来源期刊
Environmental geotechnics
Environmental geotechnics Environmental Science-Water Science and Technology
CiteScore
6.20
自引率
18.20%
发文量
53
期刊介绍: In 21st century living, engineers and researchers need to deal with growing problems related to climate change, oil and water storage, handling, storage and disposal of toxic and hazardous wastes, remediation of contaminated sites, sustainable development and energy derived from the ground. Environmental Geotechnics aims to disseminate knowledge and provides a fresh perspective regarding the basic concepts, theory, techniques and field applicability of innovative testing and analysis methodologies and engineering practices in geoenvironmental engineering. The journal''s Editor in Chief is a Member of the Committee on Publication Ethics. All relevant papers are carefully considered, vetted by a distinguished team of international experts and rapidly published. Full research papers, short communications and comprehensive review articles are published under the following broad subject categories: geochemistry and geohydrology, soil and rock physics, biological processes in soil, soil-atmosphere interaction, electrical, electromagnetic and thermal characteristics of porous media, waste management, utilization of wastes, multiphase science, landslide wasting, soil and water conservation, sensor development and applications, the impact of climatic changes on geoenvironmental, geothermal/ground-source energy, carbon sequestration, oil and gas extraction techniques, uncertainty, reliability and risk, monitoring and forensic geotechnics.
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