Ali Firat Cabalar

Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network

Uniaxial compressive strength of intact rock is significantly important for engineering geology and geotechnics, because it is an important design parameter for tunnels, rock slopes rock foundations, and it is also used as input parameter in some rock mass classification systems. This paper documents the results of laboratory experiments and numerical simulations (i.e. neural network) conducted to estimate the uniaxial compressive strength of some clay-bearing rocks selected from Turkey. Emphasis was placed on assessing the role of slake durability indices and clay contents. The input variables in developed neural network (NN) model are the origin of rocks, two/four-cycle slake durability indices and clay contents, and the output is uniaxial compressive strength. It is shown that the performance of capacities of proposed NN model is quite satisfactory. However, the NN model including four cycle slake durability index yielded slightly more precise results than that including two cycle slake durability index as input parameter. The paper also presents a comparative study on the accuracy of NN model and genetic programming (GP) in the results.

Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming

This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand-mica mixtures based on experimental results. The experimental database used for GP modelling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R2=0.95 for damping ratio and R2=0.98 for shear modulus).

Genetic programming-based attenuation relationship: An application of recent earthquakes in turkey

This study investigates an application of genetic programming (GP) for the prediction of peak ground acceleration (PGA) using strong-ground-motion data from Turkey. The input variables in the developed GP model are the average shear-wave velocity, earthquake source to site distance and earthquake magnitude, and the output is the PGA values. The proposed GP model is based on the most reliable database compiled for earthquakes in Turkey. The results show that the consistency between the observed PGA values and the predicted ones by the GP model yields relatively high correlation coefficients (R^2=0.75). The proposed model is also compared with an existing attenuation relationship and found to be more accurate.

Triaxial compression behavior of sand and tire wastes using neural networks

Tire waste additions to sand enhance the shear strength of sand for embankments. Granular and fiber shape tire wastes and their mixture with sand under drained and undrained conditions were tested in triaxial compression apparatus and modeled using neural networks (NN). In the experimental study, tire crumb and tire buffings inclusions were used at varying contents as soil reinforcement. Both quick tests and consolidated drained (CD) triaxial tests were performed to analyze the effects of tire content, tire shape, and tire aspect ratio on the shear strength of sand. Then, this extensive experimental database obtained in laboratory was used in training, testing, and prediction phases of three neural network-based soil models. The input variables in the developed NN models are tire wastes content, tire wastes type, test type, effective stress, and axial strain, and the output is the deviatoric stress. The accuracy of proposed models seems to be satisfactory. Furthermore, the proposed models are also presented as simple explicit mathematical functions for further use by researchers.

Stabilising a clay using tyre buffings and lime

The objective of this study was to evaluate the influences of both tyre buffings and lime in clay. California Bearing Ratio (CBR) and Unconfined Compression (UC) tests were conducted on the mixtures prepared with tyre buffings and lime. Tyre buffings and lime contents in the mixtures were 0%, 5%, 10%, and 15%, and 0%, 2%, 4%, and 6% by dry weight of the specimens, respectively. The response of the specimens was investigated with the inclusion of tyre buffings only, lime only, and tyre buffings and lime together. Test results revealed that addition of tyre buffings only reduces the CBR value of clay, while addition of lime only increases the CBR value of clay. Addition of a small amount of lime to the clay with tyre buffings increases the CBR values of the specimens, and thereby may cause a substantial decrease in design thickness of a highway pavement. Similar results were also obtained by the UC tests. Consequently, this research suggests to use both tyre buffings and lime as an alternative method to improve the performance of clay in an economically and environmentally beneficial way.

Neuro-fuzzy based constitutive modeling of undrained response of Leighton Buzzard Sand mixtures

This study aims to develop neuro-fuzzy (NF) based constitutive model for Leighton Buzzard Sand fraction B and Leighton Buzzard Sand fraction E mixtures using experimental data. The experimental database used for NF modeling is based on a laboratory study of saturated mixtures with various mix ratios under a 100kPa effective stress. Emphasis was placed on assessing the role of fines content in mixture and strain level on the deviatoric stress and pore water pressure generation in a 100mm diameter triaxial testing apparatus. The input variables in the developed rule based NF models are the Leighton Buzzard Sand fraction E content, and strain, and the outputs are deviatoric stress, pore water pressure generation and undrained Youngs modulus. Experimental results show that Leighton Buzzard Sand fraction B and Leighton Buzzard Sand fraction E mixtures exhibits clay-like behavior due to particle-particle effects with the increase in Leighton Buzzard Sand fraction E content. It is also shown that the performance of capacities of proposed NF models are quite satisfactory.

Triaxial behavior of sand-mica mixtures using genetic programming

This study investigates an application of genetic programming (GP) for modeling of coarse rotund sand-mica mixtures. An empirical model equation is developed by means of GP technique. The experimental database used for GP modeling is based on a laboratory study of the properties of saturated coarse rotund sand and mica mixtures with various mix ratios under a 100kPa effective stresses, because of its unusual behavior. In the tests, deviatoric stress, and pore pressure generation, and strain have been measured in a 100mm diameter conventional triaxial testing apparatus. The input variables in the developed GP models are the mica content, and strain, and the outputs are deviatoric stress, pore water pressure generation. The performance of accuracies of proposed GP based equations is observed to be quite satisfactory.

Behaviour of sand–clay mixtures for road pavement subgrade

Abstract Materials forming sand grains and colluvial soil deposits have a distinct structure, consisting of a composite matrix of coarse and fine soil grains. The influence of sand grains content on the behaviour of sand–clay mixtures was investigated by a series of intensive laboratory experiments. The California bearing ratio (CBR), unconfined compression strength (UCS) and compaction tests were carried out on various contents of sand and clay mixtures. The sand–clay mixtures were prepared with sand contents of 0, 10, 20, 30, 40, and 50% by weight. The laboratory tests on these mixtures have indicated that their behaviour will depend on the relative concentration of the sand and clay samples. The results of the tests showed a decrease in the UCS, and an increase the CBR values with an increase in the amount of sand. An increase in dry unit weight and a decrease in respective moisture content by an increase in the amount of sand were observed in the compaction tests.

Analysis of a landfill gas to energy system at the municipal solid waste landfill in Gaziantep, Turkey

This paper presents an analysis of the electricity generation from municipal solid waste (MSW), via landfill gas valorization technology, at the landfill of Gaziantep City, Turkey. Rapid increase in population, and industrial developments, throughout the world including Turkey results in larger amount of waste materials generated, increased need for energy, and adverse affects on the environment and human health. Turkey plans to produce 1/3 of its electricity demand using renewable energy sources by the year of 2023. It is recommended to use each year around 25 million tonnes of the MSW generated nationwide for a renewable energy supply. In this study, a concise summary of current status of electricity generation from a MSW landfill gas plant (via biogas harnessing) located in Gaziantep City was analyzed as a case study. Implıcatıons: MSW management has become a major challenge in Turkey, due to the increase in the amount of solid waste, which causes negative environmental impact and results in suboptimal use of raw materials and energy. An implication of waste-to-energy technology from MSW has been applied successfully in Gaziantep, where total MSW generated has increased steadily over last 15 years. The study showed the importance and benefit of energy recovery from MSW in both economic and environmental issues in Turkey.

Modelling dynamic behaviour of sand–waste tires mixtures using Neural Networks and Neuro-Fuzzy

This investigation describes the results of a series of cyclic triaxial tests on sand–waste tires mixtures, and applications of Neural Networks (NN) and Neuro–Fuzzy (NF) for the prediction of damping ratio and shear modulus of the mixtures were tested. In the cyclic triaxial testings, shear modulus and damping ratio of the sand–waste tires mixtures at various ratios have been measured for a strain range of .0001% up to .04%. Test results show that the shear modulus and damping ratio of the mixtures are strongly influenced by the waste tire inclusions. It is seen that the greater the proportion of waste tire crumbs or tire buffings on the sand, the greater is the damping ratio and the less is the shear modulus, regardless of confining pressure. The input variables in the developed NN and NF models are the (1) waste tires contents which are 0, 10, 20 and 30, (2) waste tires types which are tire crumbs and tire buffings, (3) confining pressures which are 40, 100 and 200 kPa and (4) strain level and the outputs are (1) damping ratio and (ii) shear modulus. The performance of proposed NN models (R 2 = .99 for shear modulus, and R 2 = .98 for damping ratio) is observed to be more accurate than the NF models (R 2 = .96 for shear modulus, and R 2 = 0.97 for damping ratio).