Study of the volumetric water content based on density, suction and initial water content

doi:10.1016/j.measurement.2016.08.034

Measurement

Volume 94, December 2016, Pages 531-537

https://doi.org/10.1016/j.measurement.2016.08.034 Get rights and content

Highlights

•
Importance of measuring the effect of density on SWRC is addressed.
•
Three soft computing methods for modelling density dependent SWRC are introduced.
•
GP method efficiently formulates relationship for SWRC.
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Statistical analysis reveals GP outperforms other models.
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Sensitivity and parametric analysis validates robustness of the proposed models.

Abstract

The practical application of determination of the soil water retention curves (SWRC) is in seepage modelling in unsaturated soil. The models based on the physics behind the seepage mechanism has been developed for predicting the SWRC. However, those models rarely consider the combined effects of initial volumetric water content and soil density. One of the best routes to study these effects is to formulate the SWRC models/functional relations with volumetric water content as an output and the soil density, initial volumetric water content and soil suction as input parameters. In light of this, the present work introduces the advanced soft computing methods such as genetic programming (GP), artificial neural network and support vector regression (SVR) to formulate the volumetric water content models based on the suction, density and initial volumetric water content. The performance of the three models is compared based on the standard measures and goodness-of-fit tests. The findings from the statistical validation reveals that the GP model performs the best in generalizing the volumetric water content values based on the suction, density and initial water content. Further, the 2-D and 3-D plots, evaluating the main and the interaction effects of the three inputs on the volumetric water content are generated based on the parametric procedure of the best model. The study reveals that the volumetric water content values behave non-linearly with respect to soil suction because it first decreases till a certain point of soil suction and then increases suddenly.

Introduction

Soil water retention curve (SWRC) is important for seepage modelling, which is critical for analyzing rainfall induced slope failures, artificial recharge and vegetation root water uptake [1], [2], [3], [4], [5]. SWRC is usually defined as relationship of soil volumetric water content or degree of saturation with soil suction [6], [7], [8]. The physical, mechanical and chemical property of the soil plays a significant role in affecting SWRC [9], [10], [11], [12]. Previous studies have demonstrated that the initial water content and initial dry density have a dominant effect on the SWRC [13], [14], [15], [16], [17], [18], [19], [20]. These variations may influence pore water pressure profile significantly while conducting seepage modelling [6], [7], [8], [9], [10].

The measurement of SWRC is time consuming and involves extensive laboratory procedures. Therefore, researchers have made a progress in understanding SWRC by various means of mathematical modelling. The analytical modelling of SWRC was a main focus and it was based on the soil physical, mechanical and chemical properties [21]. Another aspect of the mathematical modelling involves the use of ANN for studying the behavior of SWRC [21], [22], [23], [24]. The main reason for deploying ANN was because of its ability to learn the complex systems from only data and provides reasonable estimations. In literature, most the previous studies focused on development of ANN models based on the existing universal soil data-base [25]. However, from the above literature, it is understood that all these estimation techniques including databases of SWRCs rarely considers any combined effects of initial dry density and initial water content on SWRC. In addition, having partial or no knowledge about the soil dynamics, the possible route to understand the effect of initial dry density and initial water content on water retention curve is to formulate the models/relationships based on only the given data. In addition, to study these effects, one of the ways is to formulate the SWRC models with volumetric water content as an output and initial dry density (soil density), initial water content and soil suction as the three inputs. Statistical methods such as response surface methodology can be applied to determine the combined effect of these three inputs on the volumetric water content [26]. However, in the circumstance of uncertain and complex soil nature, the assumption of these model structures pre-hand becomes a difficult task. Alternatively, besides ANN, one advanced soft computing based on evolutionary approach of genetic programing (GP) can also be applied [26]. This algorithm differs from ANN in a way that it can evolve the functional relationships automatically without the necessity of assuming the structure of the model. The settings such as functional and terminal sets determine the diversity of model structures evolved. The ability and potential of both ANN and GP in modelling of the complex systems have been proven by its diversified applications in field of engineering [27], [28], [29].

Therefore, the present study proposes the three advanced soft computing methods (genetic programming (GP), support vector regression (SVR) and ANN) and explores their ability to develop the generalized functional relationships/models of the volumetric water content based on the three different inputs conditions (soil density, initial water content and soil suction) for a given sandy soil. Experimental data is obtained from the literature to tests the ability of the three formulated volumetric water content models. The experimental data is then input into the architect of these methods for processing and analysis. Statistical models analysis comprising of cross-validation, error metrics and hypothesis tests for the goodness of fit determines the best generalized model among the three proposed models. Further, the 2-D and 3-D plots, evaluating the main and the interaction effects of the three inputs on the volumetric water content are generated based on the parametric procedure of the best model. Sensitivity analysis based on the maximum-minimum volumetric water content values from these plots are then used to determine the significant input that contributes to volumetric water content of the sandy soil.

Section snippets

Details of experimental measurement of volumetric water content (%)

Measured results on effect of soil density and initial water content on SWRC were obtained from a comprehensive experimental study conducted by Malaya [30] on two types of geomaterials i.e., sandy soil (SA) and fly ash (FA). The physical properties of the two geomaterials in given in the study conducted by Malaya [30]. Table 1 summaries the initial compaction state of geomaterials for which SWRC was measured. In their study, continuous drying SWRC were obtained using a Perspex box equipped with

Advanced soft computing methods

The details of the mechanism and settings of the parameters of the three methods is given in this section.

Settings of the three methods

In this study, the parameter settings of GP such as population size of 300, number of generations of 200, maximum depth of gene 7, functional set including elements (sin, cos, tan, tanh, log, addition, subtraction and multiplication) and terminal set including three inputs of the data is determined by a procedure based on the trial-and-error. The best GP model (Eq. (A1) in Appendix A) is selected based on the lowest fitness value among all the runs.

SVR method is implemented by deploying the

Conclusions

This work introduces the three soft computing methods to study the effect of soil density, soil suction and initial water content on the volumetric water content. The models formulated from the three methods (GP, SVR and ANN) are analysed using the four standard measures and goodness-of-fit tests. The analysis concludes that the GP model outperformed the other two models (SVR and ANN). The functional relationship/model obtained from GP method can be used by experts to do the prediction,

Acknowledgement

This study was supported by Shantou University Scientific Research Funded Project (Grant No. NTF 16002). The authors also gratefully acknowledge the financial supports from the Macau Science and Technology Development Fund (FDCT) (Code:125/2014/A3), the National Natural Science Foundation of China (Grant no. 51508585) and the University of Macau Research Fund (MYRG2014-00175-FST).

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Study of the volumetric water content based on density, suction and initial water content

Highlights

Abstract

Introduction

Section snippets

Details of experimental measurement of volumetric water content (%)

Advanced soft computing methods

Settings of the three methods

Conclusions

Acknowledgement

Eng. Geol.

Catena

Ecol. Eng.

Eng. Geol.

Soil Till Res.

Comput. Geosci.

Chemomet. Intell. Lab. Syst.

Measurement

J. Clean. Prod.

Measurement

Measurement

Measurement

Measurement

J. Clean. Prod.

Swarm Evol. Comput.

Linear root water uptake by vegetation

Malaysian J. Civil Eng.

Desiccation of a clay by grass, bushes and trees

Geotech. Geol. Eng.

Effects of plant transpiration on suction distribution in a vegetated soil slope

Effects of the roots of Cynodon dactylon and Schefflera heptaphylla on water infiltration rate and soil hydraulic conductivity

Hydrol. Process.

Investigation of soil density effect on suction induced due to root water uptake by Schefflera heptaphylla

J. Plant Nutr. Soil Sci.