Method for optimal vertical alignment of highways

This paper presents a methodology to consider vague soil parameters required for earthwork optimisation, and to develop a genetic algorithm-based constrained curve-fitting technique required for highway vertical alignment process. The weighted ground line method is an earthwork optimisation methodology based on a hypothetical reference line and taking into account three soil properties to calculate realistic cut-fill volumes, namely swelling potential, compactibility percentage, and material appropriateness percentage. In this study, fuzzy rule-based inference methodology, which utilises previous experiences that can be expressed with linguistic terms, is employed to characterise swelling/shrinkage behaviour. In addition, material appropriateness concept is also adopted into developed optimisation methodology by a parametric algorithm using technical specifications and geotechnical data. Consequently, the genetic algorithm approach is employed for the determination of final grade line considering weighted ground elevations. The method involving an algorithm to consider the soil parameters as well as an evolutionary computation-based constrained curve-fitting technique produces outstanding geometric alignment

BUILDING AND ENVIRONMENT

Because shear strength parameters highly influence the bearing capacity of soils, several researchers have carried out large number of experimental and theoretical studies aimed at understanding soil strength behaviors. Within this context, the determination of correlations between soil index properties and shear strength parameters for specific soil types is possible. The aim of this study is to observe the performance of statistical and artificial neural network (ANN)-based methods on establishing correlations between index properties and shear strength parameters of normally consolidated plastic clays. To collect modeling data, consolidated-undrained triaxial tests were performed oil normally consolidated plastic clays obtained from the same region. Additionally, detailed statistical analyses were conducted on the test data. Results indicate that the ANN-based model is superior in determining the relationships between index properties and shear strength parameters. However, in order to get appropriate outcomes, specific care must be dedicated when applying ANN-based correlation models. (C) 2007 Elsevier Ltd. All rights reserved

Soil clustering by fuzzy c-means algorithm

WOS: 000231443400005In this study, hard k-means and fuzzy c-means algorithms are utilized for the classification of fine grained soils in terms of shear strength and plasticity index parameters. In order to collect data, several laboratory tests are performed on 120 undisturbed soil samples, which are obtained from Antalya region. Additionally, for the evaluation of the generalization ability of clustering analysis, 20 fine grained soil samples collected from the other regions of Turkey are also classified using the same clustering algorithms. Fuzzy c-means algorithm exhibited better clustering performance over hard k-means classifier. As expected, clustering analysis produced worse outcomes for soils collected from different regions than those of obtained from a specific region. In addition to its precise classification ability, fuzzy c-means approach is also capable of handling the uncertainty existing in soil parameters. As a result, fuzzy c-means clustering can be successfully applied to classify regional fine grained soils on the basis of shear strength and plasticity index parameters. (c) 2005 Elsevier Ltd. All rights reserved

Estimation of sulfate expansion level of PC mortar using statistical and neural approaches

WOS: 000237841200002Prediction of sulfate resistance is a,keynote issue for the structural evaluation of cementitious systems. Concrete structures may experience a range of sulfate attack depending on several factors. Among these, C(3)A content, C3S/C2S ratio of the cement, type of mineral admixture and its inclusion level as well as type of sulfate and its concentration may be listed. In this investigation, an experimental study was undertaken to characterize sulfate expansion of PC mortar with related parameters, and comprehensive numerical analyses were conducted for the estimation of the sulfate expansion levels. In the experimental study, twenty seven mortar mixtures were prepared and tested in accordance with ASTM C1012 testing procedure. Forty-five experimental expansion values obtained from sulfate expansion tests were statistically analyzed in detail and seventeen linear and nonlinear regression models were established for the characterization of target mapping. Apart, neural network (NN) methodology was also employed for the identification of considered nonlinear relationship. Results of this study revealed that NN model exhibited better performance over regression models to predict the sulfate expansion of various cements containing natural pozzolan and fly ash. (C) 2005 Elsevier Ltd. All rights reserved

Cuckoo Search Based Backcalculation Algorithm for Estimating Layer Properties of Full-Depth Flexible Pavements

This study introduces a backcalculation algorithm to estimate the material properties of the full-depth asphalt pavements. The proposed algorithm, namely CS-ANN, uses an Artificial Neural Network (ANN) based forward response engine, which is developed from the solutions of nonlinear finite element analysis to calculate the deflections mathematically. In the backward phase of the method, Cuckoo Search (CS), is utilized to search for the layer moduli values. The performance of the proposed method is investigated by analyzing the synthetically calculated deflections by a finite element based software and deflection data obtained from the field. In addition, to evaluate the searching capability of CS, optimization algorithms widely used in pavement backcalculation; Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA), are employed for comparison purposes. Obtained results indicate that the proposed backcalculation approach is able to determine stiffness-related layer properties in an accurate and rapid manner. In addition, CS presents a promising performance in reaching the optimum solutions that are better than GA, PSO, and GSA