Experiments and dimensional analysis of contaminated clay soils

Abstract

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Engineering Reactive Clay Systems by Ground Rubber Replacement and Polyacrylamide Treatment

“…The consistency limits, the liquid limit and flow index in particular, can be employed to infer the development of soil fabric [40,61]. An increase in the liquid limit, as is the case with PAM-treated blends (see Table 4), implies that an edge-to-face flocculated fabric dominates the matrix [62].…”

“…The increase in maximum dry density with increasing PAM concentration can be attributed to the higher viscosity of PAM solutions compared with that of water. That is, the increase in pore-fluid viscosity for the PAM-treated blends induces contact lubrication in the soil matrix; during compaction, this feature facilitates the movement and sliding of soil particles with much less effort/friction, thereby giving rise to higher maximum dry densities [44,48,61].…”

Mechanical Performance of Jute Fiber-Reinforced Micaceous Clay Composites Treated with Ground-Granulated Blast-Furnace Slag

“…Routine statistical tests, namely Fisher’s F –test and Student’s t –test, were then carried out to examine the model’s statistical significance. In addition, statistical fit-measure indices, such as the coefficient of determination R 2 (dimensionless), the root-mean-squared error RMSE (in kPa), the normalized root-mean-squared error NRMSE (in %) and the mean-absolute-percentage error MAPE (in %), were adopted to assess the model’s predictive capacity [82,83]:RMSE=1Ntrue∑b=1N[(qnormaluA)b−(qnormaluP)b] 2 false(%false) NRMSE=RMSE(qnormaluA)max−(qnormaluA)min×100 false(%false) MAPE=1Ntrue∑b=1N|1−(qnormaluP)b(qnormaluA)b|...…”

“…The partial derivative sensitivity analysis technique, as commonly adopted in the literature [43,82,84], was carried out on Equation (11) to quantify the relative impacts of the independent variables, namely F c , S c and T c , on the dependent variable q u . The overall relative impact, both positive and negative, of an independent variable, i.e., x a = F c , S c or T c , on the dependent variable q u , commonly referred to as sensitivity, can be defined as:Sfalse(xafalse)=σfalse(xafalse)Nσfalse(qufalse)×true∑b=1N|Dab|∋Da=dqudxawhere D a = partial derivative of q u or Equation (11) with respect to x a = F c , S c or T c ; σ ( x a ) = standard deviation of x a data; σ ( q u ) = standard deviation of predicted q u data; b = index of summation; and N = number of observations ( N = 28, as outlined in Table 5).…”

“…Routine statistical tests, namely Fisher’s F –test and Student’s t –test, were then carried out to examine the model’s statistical significance. In addition, statistical fit-measure indices, such as the coefficient of determination R 2 (dimensionless), the root-mean-squared error RMSE (in kPa), the normalized root-mean-squared error NRMSE (in %) and the mean-absolute-percentage error MAPE (in %), were adopted to assess the model’s predictive capacity [ 82 , 83 ]: where q u A = actual peak UC strength, as presented in Figure 5 ; q u P = predicted peak UC strength; b = index of summation; and N = number of experimental data points used for model development ( N = 28, as outlined in Table 5 ).…”