Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Influence of geocell reinforcement on uplift response of belled piles

Piles are frequently used to transfer the heavy compressive loads to strong soil layers located in the depth of bed. In addition, such piles may be subjected to combination of repeated compressive and tensile loads due to earthquake, wind, etc. This paper describes a series of laboratory model tests, at unit gravity, performed on belled pile, embedded in unreinforced and geocell-reinforced beds. The tests were performed to evaluate the beneficial effect of geocell in decreasing the downward and upward displacements and performance improvement of the uplift response of belled pile under repeated compressive and tensile loads. Pile displacements due to fifty load repetitions were recorded. The influence of the height of geocell above the bell of pile, an additional geocell layer at the base of belled pile, and the number of load cycles on pile displacements were investigated. The test results show that the geocell reinforcement reduces the magnitude of the final upward displacement. It also acts as a displacement retardant, and changes the behaviour of belled pile from unstable response condition due to excessive upward pile displacement in unreinforced bed to approximately steady response condition. Therefore, the geocell reinforcement permits higher tensile loads or increased cycling. The efficiency of reinforcement in reducing the maximum upward displacement of the pile (i.e. pull-out resistance) was increased by increasing the height of geocell above the bell of the pile. Furthermore, the comparison showed that a specific improvement in upward and downward displacement and the stability against uplift can be achieved using an additional geocell layer at the base. The geocell reinforcement may reduce the required length of pile shaft, consequently reducing required excavation, backfill, and pile’s material. Simple dimensional analysis showed the need for an increased stiffness of the geosynthetic components in order to match prototype-scale performance similitude.     

Numerical study of soil heterogeneity effects on contaminant transport in unsaturated soil (model development and validation)

The movement of chemicals through soil to groundwater is a major cause of degradation of water resources. In many cases, serious human and stock health implications are associated with this form of pollution. The study of the effects of different factors involved in transport phenomena can provide valuable information to find the best remediation approaches. Numerical models are increasingly being used for predicting or analyzing solute transport processes in soils and groundwater. This article presents the development of a stochastic finite element model for the simulation of contaminant transport through soils with the main focus being on the incorporation of the effects of soil heterogeneity in the model. The governing equations of contaminant transport are presented. The mathematical framework and the numerical implementation of the model are described. The comparison of the results obtained from the developed stochastic model with those obtained from a deterministic method and some experimental results shows that the stochastic model is capable of predicting the transport of solutes in unsaturated soil with higher accuracy than deterministic one. The importance of the consideration of the effects of soil heterogeneity on contaminant fate is highlighted through a sensitivity analysis regarding the variance of saturated hydraulic conductivity as an index of soil heterogeneity. Copyright © 2011 John Wiley & Sons, Ltd.     

Modification of the GALDIT framework using statistical and entropy models to assess coastal aquifer vulnerability

Recently, groundwater vulnerability assessment of coastal aquifers using the GALDIT framework has been widely used to investigate the process of groundwater contamination. This study proposes multi-attribute decision-making (MADM) entropy and Wilcoxon non-parametric statistical test methods to improve the vulnerability index of coastal aquifers. The rates and weights of this framework were modified using Wilcoxon non-parametric and entropy methods, respectively, and a combined framework of GALDIT-entropy, Wilcoxon-GALDIT, and Wilcoxon-entropy was obtained. Pearson correlation coefficients between the mentioned vulnerability indices and total-dissolved solids (TDS) of 0.51, 0.66 and 0.75, respectively, were obtained. According to the results, the Wilcoxon-entropy index had the highest correlation with TDS. Generally, it can be concluded that the proposed frameworks provide a more accurate estimation of vulnerability distribution in coastal aquifers.     

Evaluating the support vector machine for suspended sediment load forecasting based on gamma test

Due to the various influencing factors on river suspended sediment transportation, determining an appropriate input combination for developing the suspended sediment load forecasting model is very important for water resources management. The influence of pre-processing of input variables by Gamma Test (GT) was investigated on performance of Support Vector Machine (SVM) with two kernels; Radial Basis Function (RBF) and polynomial in order to forecast daily suspended sediment amount in the period between 1983 and 2014 at Korkorsar basin, northern Iran. The best input combination was identified using GT and correlation coefficient analysis. Then, the SVM model was developed and the suspended sediment amount was forecasted with RBF and polynomial kernels. The obtained results in testing phase showed that GT-SVM (RBF kernel) model can estimate suspended sediment more accurately with the lowest RMSE (14.045 ton/day), highest correlation coefficient (0.88) and highest NSEC coefficient (0.88) than SVM (RBF kernel) model (RMSE?=?18.36ton/day, \( {R}^2=0.79, \) \( NSEC=0.73 \)) and had a better performance than the other models. The results indicated that in forecasting the first nine maximum values of suspended sediment load, GT-SVM (RBF) had a higher capability than the other models and could provide a more accurate estimation from the maximum rate of suspended sediment. The results of this study showed the capability of identifying the priority of the input parameters can change GT to a useful and technical test for input variables pre-processing to forecast the amount of suspended sediments.     

Mechanical Behavior of a Clay Soil Reinforced with Nylon Fibers

Soft soils are well known for their low strength and high compressibility. Several techniques, including reinforcement, are commonly used to increase the strength and decrease the deformation of this kind of soil. This paper presents the results of an investigation into the effects of fiber on the consolidation and shear strength behavior of a clay soil reinforced with nylon fibers. A series of one dimensional consolidation and triaxial tests were conducted on samples of unreinforced and reinforced clay with different percentages of randomly distributed nylon fibers. The results show that the preconsolidation pressure decreases and the coefficient of swelling and compression generally increase with increasing the fiber content. Furthermore, the addition of the fiber leads to a significant increase in shear strength and friction angle of the natural soil.     

A hybrid multiple criteria decision-making model for the sustainable management of aquifers

In this study, a hybrid multiple criteria decision-making (HMCDM) model was proposed for prioritizing scenarios for managing groundwater use from an aquifer. Three scenarios, including the construction of subsurface dams, the use of artificial recharge and reducing groundwater use by 5% and 10% were considered to assess the most sustainable development approach. The examined MCDM models were: simple additive weighting (SAW); and MTAHP which is a hybridization of the modified TOPSIS and the analytic hierarchy process models. The criteria proposed for determining the order preference of the scenarios included the sustainable development index (I_U) and a modified water exploitation index as well as economic, social and environmental indices. To assess the technical and economic impacts of the management scenarios, modeling of the aquifer was simulated for a 3-year period using these scenarios. The results of the assessment indicated that the scenario of water withdrawal reduction by 10% was the best scenario determined in MTAHP followed by a reduction in groundwater withdrawal by 5%, the use of artificial recharge and the construction of a subsurface dam, respectively. The difference between the results of MTAHP and SAW models was in their first and third ranks, in such a way artificial recharge scored the first rank in SAW model and the third rank in MTAHP model, also withdrawal reduction by 10% scored third rank in SAW model and first rank in MTAHP model. The results of these two models have demonstrated that the construction of a subsurface dam in Shahrekord aquifer is not an appropriate management option. According to the results of this study, MTAHP models can be applied for ranking feasible management scenarios in aquifers using the redefined sustainable development and modified groundwater exploitation indices introduced in this study.