This paper presents a review of two models (i.e., arching and lateral squeezing) developed for predicting earth pressures in soil-bentonite (SB) cutoff walls. The assumptions of these existing models are discussed, a modified lateral squeezing (MLS) model is presented, and all three models are compared based on predicted horizontal stresses for representative field conditions. Each model predicts that the stress distribution within a SB cutoff wall may be considerably lower than a geostatic distribution, particularly at depth. The arching model yields the lowest stress distribution but may underestimate the true distribution due to the assumption of rigid trench sidewalls. The MLS model (1) allows sidewall deformation and (2) accounts for the stress-dependent nature of SB backfill compressibility. The study also finds that additional model development is needed to characterize the stress state of a SB cutoff wall in three dimensions.

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R 2 ) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.

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