Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction

Abstract

The reinforcement of stabilized soils with fibers arises as an interesting technique to overcome the two main limitations of the stabilized soils: the weak tensile/flexural strength and the higher brittleness of the behavior. These types of mixtures require extensive laboratory characterization since they entail the study of a great number of parameters, which consumes time and resources. Thus, this work presents an alternative approach to predict the unconfined compressive strength (UCS) and the tensile strength of soil–binder–water mixtures reinforced with short fibers, following a Machine Learning (ML) approach. Four ML algorithms (Artificial Neural Networks, Support Vector Machines, Random Forest and Multiple Regression) are explored for mechanical prediction of reinforced soil–binder–water mixtures with fibers. The proposed models are supported on representative databases with approximately 100 records for each type of test (UCS and splitting tensile strength tests) and on the consideration of sixteen properties of the composite material (soil, fibers and binder). The predictive models provide an accurate estimation (R2 higher than 0.95 for Artificial Neuronal Networks algorithm) of the compressive and the tensile strength of the soil–water–binder–fiber mixtures. Additionally, the results of the proposed models are in line with the main experimental findings, i.e., the great effect of the binder content in compressive and tensile strength, and the significant effect of the type and the fiber properties in the assessment of the tensile strength.

Combined Effect of Biopolymer and Fiber Inclusions on Unconfined Compressive Strength of Soft Soil

“…It can be seen from Figure 5 that, regardless of biopolymer content, the stress-strain curve exhibits post-peak softening behavior with increasing fiber content. This is because, at the post-peak stage, the relatively large compressive strain enables the full mobilization of tensile resistance of the fibers, which renders the specimens more ductile and hence improves the residual strength of the soil [40,41]. The test results show that the strength of biopolymer-fiber-stabilized soil varies with the content of each reinforcement material, curing time, and curing conditions.…”

Prediction of Elastic Modulus for Fibre-Reinforced Soil-Cement Mixtures: A Machine Learning Approach

“…Examples of problematic soils include expansive soils, which are commonly observed due to their worldwide occurrence except in the Artic region [1,3], loess or sandy soils, etc. These soils usually exhibit low shear strength, high compressibility, high susceptibility to volume changes, and sensitivity to moisture content [2,4]. Therefore, there is a need to improve the natural properties of such soils for construction purposes in a process called soil stabilisation.…”

“…Physical soil stabilisation methods include compaction, consolidation, mixing soils of different grain size to obtain a well-graded soil for construction purposes, and finally removal of existing soil and replacing it with a natural soil deposit suitable for construction [1,5,6]. Soil chemical stabilisation methods have been used in recent decades [4,[7][8][9][10][11] to improve slopes, foundation structures, increase slope stability, prevent liquefaction, and stabilise contaminated soils, among others [9,12,13].…”

Trends and Prospects in Geotechnics

“…The fourth [5] and ninth [6] papers are focussed on the study of machine learning algorithms as a tool to accurately predict the geomechanical properties of rock or soils, thus mimimizing the costs associated with the pre-design and design stages of geotechnical structures. In the fourth paper, Ahmad et al [5] investigate supervised machine learning algorithms (support vector machine, random forest, AdaBoost, and k-nearest neighbour) to predict the rockfill material shear strength.…”

“…The SVM model results in the best and highest performance algorithm, which suggests that this algorithm is more robust in comparison with others in rockfill material shear strength prediction. On the other hand, in the ninth paper, Tinoco et al [6] study the performance of four machine learning algorithms (artificial neural networks, support vector machines, random forest, and multiple regression) to predict the unconfined compressive strength and the tensile strength of soil-binder-water mixtures reinforced with short fibres. Exploring global sensitivity analysis ensured a deeper understanding around the proposed algorithms.…”