Hamza Güllü

Prediction of compressive and tensile strength of limestone via genetic programming

Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey.

Microtremor Measurements for the Microzonation of Dinar

Abstract — The geotechnical site conditions in Dinar town located in western Turkey were investigated after the 1995 Dinar earthquake based on borings, in situ penetration tests, seismic wave velocity measurements, and microtremor records. The variation of damage distribution within the town was evaluated with respect to 23 district damage ratios calculated, based on the detailed damage survey conducted by the General Directorate of Disaster Affairs. Site amplifications were estimated from microtremor spectral ratios and microzonation was performed using a GIS methodology. The results of in situ penetration tests and seismic wave velocity measurements as well as the damage distribution were compared with the amplification zonation obtained from microtremor records. The results indicate the applicability of microtremor spectral ratios for assessing the local site conditions and site amplifications.

Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming

In this paper, two soft computing approaches, which are known as artificial neural networks and Gene Expression Programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The parameters, “ultrasound pulse velocity”, “water absorption”, “dry density”, “saturated density”, and “bulk density” which are experimentally determined based on the procedures given in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict “uniaxial compressive strength” and “tensile strength” of Gaziantep basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The results obtained are also useful in characterizing the Gaziantep basalts for practical applications.

Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom ash

In order to understand the treatment of a marginal soil well, the underlying input-output relationship on the strength and elastic responses due to nonlinearity has always been a great importance in the stabilized mixtures for an optimal design. This paper employs a relatively new soft computing approach, genetic expression programming (GEP), to formulations for unconfined compressive strength (UCS) and elasticity modulus (Es) of clay stabilized with bottom ash, using a database obtained from the laboratory tests conducted in the study. The predictor variables included in the formulations are bottom ash dosage, dry unit weight, relative compaction, brittleness index and energy absorption capacity. The results demonstrate that the GEP-based formulas of UCS and Es are significantly able to predict the measured values to high degree of accuracy against the nonlinear behavior of soil (p 0.847). The GEP approach is found to have a better correlation performance as compared with the nonlinear regression as well. The sensitivity analysis for the parameter importance shows that the dominant influence on the predictions of UCS and Es is exerted by the variables of bottom ash dosage and energy absorption capacity. This study reveals that the GEP is a potential tool for establishing the functions and identifying the key variables for predicting the strength and elastic responses of the clay treated with bottom ash. Including a waste material in the proposed formulas can also serve to the environment for the development of recycling and sustainability.

On the prediction of shear wave velocity at local site of strong ground motion stations: an application using artificial intelligence

Since the determination from experimental tests are expensive and time consuming, the site conditions in strong ground motion equations are mostly expressed by geologically qualitative descriptions of soils at the recording stations. The analytical solution for the site description has not been sufficiently studied due to highly nonlinear behavior of soil. Advances in field of artificial intelligence (AI) offer new insights to solve the problems in the most complex systems utilizing different algorithms and models. This paper primarily aims to predict average shear wave velocity (

Use of factorial experimental approach and effect size on the CBR testing results for the usable dosages of wastewater sludge ash with coarse-grained material

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Use of ranking measure for performance assessment of correlations for the compression index

) as a soil property at the earthquake recording stations by applying AI methods, which are composed of artificial neural network (ANN) and genetic expression programming (GEP). The application is performed for the 60-accelerograph station sites located in California, USA. The predictor variables of