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This study examined the effect of mechanical and chemical improvement on deltaic lateritic soils in Warri East in Delta State, Nigeria. Mechanical stabilization was carried out by adding river sand to the natural soil in various proportions, while chemical stabilization was carried out using cement and a mixture of cement and sand. Compaction and CBR tests were conducted on the natural soil before and after stabilization. From the results obtained, it was seen that the mechanical stabilization method improved the strength properties of the soil making it suitable for use as subbase materials, though not as much as the chemical stabilization method or the mixed method of stabilization. It was concluded that using a combination of cement and sand as a stabilizing agent for deltaic lateritic soils can lead to significant reduction in the amount of cement required for soil stabilization thus saving costs. [ABSTRACT FROM AUTHOR]

The Niger Delta Region of Nigeria is within the mangrove forest region and is crisscrossed by series of streams and creeks. As a result of the high rainfall volume within this region there is a tendency for severe flooding to occur. These flood events have severe consequences on lives and properties. It is therefore necessary to carry out a flood frequency analysis in this area in order to predict the potentials for the occurrence of flooding and take necessary measures for their remediation. In this study, peak monthly rainfall for thirty three years from 1981 - 2013 were acquired for Patani area in the Niger Delta region of Nigeria from NIMET and CBN. Five probability distributions, namely Normal, Log normal, Log-Pearson, Gumbel and Foster's Type -1 were selected for frequency analysis of the precipitation data using return periods of 2, 5, 10, 25, 50, 100 and 200 years respectively. The predicted values were subjected to goodness of fit tests using ANFIS tool in the MATLAB 7.0 software. The results of the tests and analyses show that the Gumbel Distribution best describes the precipitation data for the region and can therefore be used for the prediction of flooding within the study area followed by the Foster's Type- I distribution. The Normal Distribution was on the other hand found the least suitable for describing the precipitation data for Patani area. [ABSTRACT FROM AUTHOR]

This research work seeks to develop models for predicting the shear strength parameters (cohesion and angle of friction) of lateritic soils in central and southern areas of Delta State using artificial neural network modeling technique. The application of these models will help reduce cost and time in acquiring geotechnical data needed for both design and construction in the study area. A total of eighty-three (83) soil samples were collected from various locations in Delta State of Nigeria. The geotechnical soil properties were determined in accordance with British Standards. The range of the angle of internal friction and cohesion obtained from the tests are 2 to 43 degrees and 3 to 82 kN/m² respectively. The optimum artificial neural network architecture network was found to be 3-9-1, that is three inputs, nine hidden layer nodes, and one output node for cohesion. While, the angle of friction had an optimal network geometry of 3-11-1, that is three inputs, eleven hidden layer nodes, and one output node. The results of the coefficient of determination and root mean square showed that the artificial neural network method outperforms some selected empirical formulae in the prediction of shear strength parameters. [ABSTRACT FROM AUTHOR]

The flood frequency analysis of River Okhuwan at Ugonoba site in Benin Owena River Basin in Nigeria was carried out using annual maximum stream flow series for 20 years (1989-2008). The objective of the study was to determine the best fit probability distribution model applicable to the site from amongst three selected 3-parameter distribution models namely: Generalized Extreme Value distribution (GEV), Generalized Logistic distribution (GLO) and Generalized Pareto distribution (GPA) whose parameters were estimated by the method of L-moments. The best fit distribution was selected based on result of four goodness-of-fit tests applied to the candidate distributions. The results indicate that GPA is the best fit distribution model for analyzing the annual maximum flood series at the study site for return period less than or equal to 25 years but for return periods between 25 years and 200 years, GLO or GEV is recommended in that order. [ABSTRACT FROM AUTHOR]