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Properties Of Concrete

Probability and statistics in Civil Engineering: by G.N. Smith, Nichols Publishing Company, New York, NY, 1986, 244 pp.

Research highlights? California Bearing Ratio estimated with SRA, MRA and of ANN models. ? Regression analysis and artificial neural network estimation indicated strong correlations between the sieve analysis, Atterberg limits, maximum dry unit weight and optimum moisture content. ? The proposed correlations will be useful for a preliminary design of a project. This study presents the application of different methods (simple-multiple analysis and artificial neural networks) for the estimation of the California bearing ratio (CBR) from sieve analysis, Atterberg limits, maximum dry unit weight and optimum moisture content of the soils. The resistance of granular soils, which are in the superstructure foundation and subgrade layers are usually tested by CBR (California bearing ratio), which is an old and still extensively used experiment. The data were collected from the public highways of Turkey's different regions. Regression analysis and artificial neural network estimation indicated strong correlations (R2=0.80-0.95) between the sieve analysis, Atterberg limits, maximum dry unit weight (MDD) and optimum moisture content (OMC). It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.

Estimation of California bearing ratio by using soft computing systems

On the swelling potential of compacted high plasticity clays

Compression index Cc is an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge. This paper suggests a novel modelling approach using machine learning (ML) technique. The performance of five commonly used machine learning (ML) algorithms, i.e. back-propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), random forest (RF) and evolutionary polynomial regression (EPR) in predicting Cc is comprehensively investigated. A database with a total number of 311 datasets including three input variables, i.e. initial void ratio e0, liquid limit water content wL, plasticity index Ip, and one output variable Cc is first established. Genetic algorithm (GA) is used to optimize the hyper-parameters in five ML algorithms, and the average prediction error for the 10-fold cross-validation (CV) sets is set as the fitness function in the GA for enhancing the robustness of ML models. The results indicate that ML models outperform empirical prediction formulations with lower prediction error. RF yields the lowest error followed by BPNN, ELM, EPR and SVM. If the ranges of input variables in the database are large enough, BPNN and RF models are recommended to predict Cc. Furthermore, if the distribution of input variables is continuous, RF model is the best one. Otherwise, EPR model is recommended if the ranges of input variables are small. The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.

Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

Prediction of artificial soil's unconfined compression strength test using statistical analyses and artificial neural networks

The possibility of predicting the Los Angeles (L.A.) abrasion loss from the Schmidt hammer, point load and porosity tests was investigated using 9 igneous, 11 metamorphic and 15 sedimentary rocks with L.A. values ranging from 10 to 76%. Regression analysis indicated a strong correlation (r2 = 0.72) between L.A. abrasion loss and point load and a good correlation (r2 = 0.62) between L.A. abrasion loss and Schmidt hammer value. When the samples were divided according to porosity (n 1%), stronger correlations were obtained, especially for the point load index. The predictions from the Schmidt hammer results were not as good but within acceptable limits.

Empirical methods to predict the abrasion resistance of rock aggregates

Soil structure changes during compaction of a cohesive soil

Undisturbed and disturbed samples of cohesive soils were collected from eight different locations to investigate the possibility of estimating the Atterberg limits of cohesive soils from P-wave vel...

Correlations between P-wave velocity and Atterberg limits of cohesive soils

Some challenging studies are experimentally applied for characterizing parameters in Proctor compaction tests. Compression of a fill is mechanically done in Compaction process. Compaction is a physical process which gets the soil into a dense state. Improving the shear strength and decreasing the compressibility and permeability of the soil can be done with this physical process. Support Vector Machine (SVM) is a popular method due to its performance today. This method is commonly employed in the regression analysis as well as being used in the classification process. In this study, SVM was employed to predict of compaction parameters (maximum dry unit weight and optimum moisture content) without making any experiments in a soil laboratory. In the study, more than a hundred compaction data collected from the small dams in central Anatolia region was employed. In the study, R errors are satisfied (0.92 and 0.89) for SVM models. Consequently, the proposed regression analysis with SVM is useful for model design of the projects in where there are limitations as financial and temporal.

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Osman Günaydin

Papers

Prediction of artificial soil's unconfined compression strength test using statistical analyses and artificial neural networks

Empirical methods to predict the abrasion resistance of rock aggregates

Soil structure changes during compaction of a cohesive soil

Correlations between P-wave velocity and Atterberg limits of cohesive soils

Regression Analysis of Soil Compaction Parameters Using Support Vector Method