Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing
TL;DR: In this article, the results of 90 Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) tests on sulfate silty sand stabilized with different lime and microsilica percentages as the two main stabilizers.
About: This article is published in Soils and Foundations.The article was published on 2018-02-01 and is currently open access. It has received 62 citations till now.
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TL;DR: In this article, industrial wastes such as Granulated Blast Furnace Slag (GBFS) and Basic Oxygen Furnace SLag (BOFS) activated with calcium oxide (CaO) and medium reactive magnesia (MgO) are used for chemical stabilization of a soft clay.
81 citations
TL;DR: The results indicate that ML models outperform empirical prediction formulations with lower prediction error and the predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.
Abstract: Compression index Cc is an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge. This paper suggests a novel modelling approach using machine learning (ML) technique. The performance of five commonly used machine learning (ML) algorithms, i.e. back-propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), random forest (RF) and evolutionary polynomial regression (EPR) in predicting Cc is comprehensively investigated. A database with a total number of 311 datasets including three input variables, i.e. initial void ratio e0, liquid limit water content wL, plasticity index Ip, and one output variable Cc is first established. Genetic algorithm (GA) is used to optimize the hyper-parameters in five ML algorithms, and the average prediction error for the 10-fold cross-validation (CV) sets is set as the fitness function in the GA for enhancing the robustness of ML models. The results indicate that ML models outperform empirical prediction formulations with lower prediction error. RF yields the lowest error followed by BPNN, ELM, EPR and SVM. If the ranges of input variables in the database are large enough, BPNN and RF models are recommended to predict Cc. Furthermore, if the distribution of input variables is continuous, RF model is the best one. Otherwise, EPR model is recommended if the ranges of input variables are small. The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.
70 citations
TL;DR: It can be concluded that the newly constructed ELM-based ANSI models can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.
57 citations
TL;DR: The proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects, and has the most accurate prediction in predicting the soaked CBR at all stages.
45 citations
TL;DR: In this article, the authors explored the potential of an evolutionary algorithm, i.e., genetic algorithm (GA), and a hybrid intelligent approach combining neural network with GA (ANN-GA), to estimate the resilient modulus (Mr) of cohesive pavement subgrade soils.
41 citations
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01 Jan 1950
TL;DR: In this paper, the authors present an approach for estimating the average risk of a risk-optimal risk maximization algorithm for a set of risk-maximization objectives, including maximalaxity and admissibility.
Abstract: Preface to the Second Edition.- Preface to the First Edition.- List of Tables.- List of Figures.- List of Examples.- Table of Notation.- Preparations.- Unbiasedness.- Equivariance.- Average Risk Optimality.- Minimaxity and Admissibility.- Asymptotic Optimality.- References.- Author Index.- Subject Index.
4,382 citations
TL;DR: In this article, the authors studied the stabilization of residual soils by chemically using cement and rice husk ash and concluded that adding 6-8% and 10-15% of these materials to the residual soil is an optimum amount.
482 citations
TL;DR: The new hybrid regression method, termed Evolutionary Polynomial Regression (EPR), overcomes shortcomings in the GP process, such as computational performance; number of evolutionary parameters to tune and complexity of the symbolic models.
Abstract: This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to employ parameter estimation to obtain constants using least squares. The new technique, termed Evolutionary Polynomial Regression (EPR) overcomes shortcomings in the GP process, such as computational performance; number of evolutionary parameters to tune and complexity of the symbolic models. Similarly, it alleviates issues arising from numerical regression, including difficulties in using physical insight and over-fitting problems. This paper demonstrates that EPR is good, both in interpolating data and in scientific knowledge discovery. As an illustration, EPR is used to identify polynomial formulae with progressively increasing levels of noise, to interpolate the Colebrook-White formula for a pipe resistance coefficient and to discover a formula for a resistance coefficient from experimental data.
343 citations
TL;DR: In this paper, an artificial neural network (ANN) was used to predict the compressive strength of recycled aggregate concrete using 14 input parameters that included: the mass of water, cement, sand, natural coarse aggregate, recycled coarse aggregate used in the mix designs, water to cement ratio of concrete, fineness modulus of sand, water absorption of the aggregates, saturated surface-dried (SSD) density, maximum size, and impurity content of recycling coarse aggregate by volume, and the coefficient of different concrete specimen.
306 citations
TL;DR: The effectiveness of fly ash use in the stabilization of organic soils and the factors that are likely to affect the degree of stabilization were studied in this paper, where unconfined compression and resilient modulus tests were conducted on organic soil.
Abstract: The effectiveness of fly ash use in the stabilization of organic soils and the factors that are likely to affect the degree of stabilization were studied. Unconfined compression and resilient modulus tests were conducted on organic soil–fly ash mixtures and untreated soil specimens. The unconfined compressive strength of organic soils can be increased using fly ash, but the amount of increase depends on the type of soil and characteristics of the fly ash. Resilient moduli of the slightly organic and organic soils can also be significantly improved. The increases in strength and stiffness are attributed primarily to cementing caused by pozzolanic reactions, although the reduction in water content resulting from the addition of dry fly ash solid also contributes to strength gain. The pozzolonic effect appears to diminish as the water content decreases. The significant characteristics of fly ash that affect the increase in unconfined compressive strength and resilient modulus include CaO content and CaO/SiO2...
228 citations