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Hydro-Mechanical Properties of an Unsaturated Frictional Material

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Chapter 3<br />

Introduction to Process Modeling -<br />

A Statistical Approach<br />

3.1 General<br />

Direct determination <strong>of</strong> the relation between suction <strong>an</strong>d volumetric water content, saturation<br />

or gravimetric water content from experiments on s<strong>an</strong>d are difficult to h<strong>an</strong>dle due to the<br />

narrow soil suction within which the drainage <strong>an</strong>d vice versa imbibition process take place.<br />

That is the relev<strong>an</strong>t r<strong>an</strong>ge <strong>of</strong> suctions (e.g. between 0 to 20 kPa) to be measured is relatively<br />

small but the measurement error <strong>of</strong> the instrument (e.g. tensiometer) may be large. For<br />

this reason the assessment <strong>of</strong> the error <strong>an</strong>d model validation are <strong>of</strong> critical import<strong>an</strong>ce in<br />

experimental building up the soil-water characteristic curve for s<strong>an</strong>d.<br />

Most models suggested in literature for prediction <strong>of</strong> the hysteretic nature <strong>of</strong> the soil-<br />

water characteristic curve are not trivial to use or do not fit the experimental data very well<br />

(see also comparison <strong>of</strong> experimental <strong>an</strong>d predicted data <strong>of</strong> s<strong>an</strong>d in Chapter 7). Therefore in<br />

the present chapter a method is proposed that c<strong>an</strong> be used to develop suction-water content<br />

model based on statistical <strong>an</strong>alysis for experimental suction (i.e. total suction, matric suction,<br />

osmotic) <strong>an</strong>d water content (saturation, gravimetric water content) results.<br />

3.2 Steps <strong>of</strong> Model Building<br />

The main steps to be done for building <strong>an</strong> appropriate model are given in Fig. 3.1. Basic<br />

assumption for process modeling is the availability <strong>of</strong> experimental results. However, in the<br />

model selection step diagrams <strong>of</strong> the experimental data, knowledge <strong>of</strong> the process <strong>an</strong>d assump-<br />

tions about the process are collected to determine the form <strong>of</strong> the model, that will be used<br />

for curve fitting. Then the selected model <strong>an</strong>d possible information about the experimental<br />

data, assumptions regarding to the process <strong>an</strong>d a convenient curve fit method are used to<br />

estimate the unknown parameters in the model. Using the model parameter estimates <strong>an</strong>d<br />

the experimental data, the model is assessed wether or not the model assumptions are valid.<br />

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