Charnockites are a distinctive type of granite characterized by coarse-grained texture and the presence of pleochroic hypersthene and perthitic feldspars. They occur worldwide mainly in the Precambrian shield areas but are not as abundant as other rocks of granitic affinity. Laboratory test results for three charnockites from Nigeria indicate good quality materials that are fairly comparable in many aspects with the more tried and tested granites. However, the coarse-grained texture, the relatively high percentage loss values in the aggregate crushing value test as well as the Los Angeles abrasion and sulphate soundness values, which only just meet acceptance limits seem to indicate possible poor performance, particularly where they are to be subjected to significant impact, cavitation and scour. The compressive strengths of the samples fall below 200 MPa, which is generally considered a good average value for aggregate for most civil engineering works. However, the disparity between test results and performance records of some rocks, local availability of a rock and the high ratio of transportation to production costs of crushed stone all militate against any disadvantage these somewhat poor test results may indicate charnockites have when compared with more established rocks.

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/m 2 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.

No abstract

No abstract

The grain size analysis and the Atterberg limit values indicate that the Ajali Sandstone falls into the A-3 and SP-SW classes of the AASHTO and the Unified Soil Classification systems respectively. The low values of the coefficient of uniformity 2.9-3.4 show the deposit is uniformly graded and the soil is deficient in silt-clay fractions. At the optimum moisture content of 8% the untreated soil has a maximum dry density of 1.77 Mg/m3. The CBR of the soil when compacted to the standard Proctor optimum and soaked for three days is 10%. To establish the effective cement requirement of the soil, 5, 7, 9 and 110y weight of Portland cement was added to the soil. Results show that 80f the stabilizer meets the required minimum CBR of 100% and the 7-day uniaxial compressive strength of 1.4MPa (l.4 N/mm2). On ageing, the stabilized A-3 soil gains appreciably in strength.