Microanalysis and Compactive Efforts Study of Black Cotton Soil Treated With Cement Kiln Dust

An expansive clay, also known as Black cotton soil (BCS) was treated with up to 10 % cement kiln dust (CKD) using three different compactive efforts; British Standard light (BSL), West African Standard (WAS) and British Standard heavy (BSH) energies. Laboratory tests were performed on the natural soil and CKD treated soil samples in accordance with BS 1377 (1990) and BS 1924 (1990) respectively with the aim of improving the deficient soil to meet engineering requirements. Preliminary evaluations on the natural black cotton soil showed that it fell under A-7-6 (16) using AASHTO classification and CL according to Unified Soil Classification System (USCS). Results of laboratory tests carried out on soil specimens show that the properties of the soil generally improved with CKD treatment. Peak unconfined compressive strength (UCS) values of 357.07, 382.49 and 528.82 kN/m2 and California bearing ratio (CBR) of 7, 10 and 19 % as well as resistance to loss in strength of 44, 55 and 55 % were recorded at 10 % CKD treatment, respectively, for BSL, WAS and BSH compactive energies. Reduction in the particle sizes with curing period was observed when samples were viewed through the scanning electron microscope (SEM). The study showed that CKD can be beneficially used to improve the subgrade of lightly trafficked roads and as admixture in lime stabilization during construction of flexible pavements over expansive soil.Keywords: California bearing ratio; Cement kiln dust, Durability; Expansive soil; Microanalysis, Unconfined compressive strength; Scanning electron microscope

Influence of Delay after Mixing on Compaction Characteristics of Cement- Locust Bean Waste Ash Modified Lateritic Soil

A lateritic soil modified with up to 4% of ordinary Portland cement (OPC) and up to 8% of locust bean waste ash (LBWA) by dry  weight of soil was evaluated for use in road construction. Lateritic soil – OPC - LBWA mixtures were delayed for up to 3 hours aftermixing before compaction at British Standard light (BSL) energy, thus simulating the likely delay in the placement of the mixtures that might occur in the field. Results obtained show that maximum dry density (MDD) values of the natural soil increased from 1.62 to 1.79Mg/m3 at 3 hours delay after mixing when treated with 4% OPC/6% LBWA, while the optimum moisture content (OMC) values decreased from 16.2 to a minimum of 11.33% after 3 hours when the soil was treated with 2% OPC/4% LBWA blend. Statistical analysis was carried out on results obtained using analysis of variance (ANOVA) with the Microsoft Excel Analysis Tool Pak Software Package to determine the levels of significance of effect of cement/ and LBWA on the properties of the soil. The effects of cement and LBWA from the results obtained were not statistically significant on the compaction characteristics. Based on the results obtained from the study, it is recommended that an optimal mix of 2% OPC/6% LBWA be used for the modification of lateritic soil and should not be placed more than 2 hours after mixing for the construction of sub-base and base of lightly trafficked roads. ©University of Ibadan Keywords: Compaction, Delay, Lateritic soils, Locust bean waste as

Effect of Cement–Locust Bean Waste Ash Blend on the Gradation and Plasticity Characteristics of Modified Lateritic Soil

A lateritic soil was treated with a blend of up to 4 % ordinary Portland cement (OPC) and up to 8% locust bean waste ash (LBWA) by dry weight of soil to investigate its effect on the gradation and plasticity characteristics of the modified soil. Test results generally show that the gradation and plasticity characteristics of the modified soil improved with higher concentrations of the OPC/LBWA blend. Based on Nigerian General Specification plasticity requirement, an optimal 1 % OPC/8 % LBWA blend can be used to modify the lateritic soil for use as a sub base material in road construction. Keywords: Cement, Gradation, Lateritic soil, Locust bean waste ash, Modification, PlasticityNigerian Journal of Technological Research, 8(2), 201

Selected AI optimization techniques and applications in geotechnical engineering

AbstractIn an age of depleting earth due to global warming impacting badly on the ozone layer of the earth system, the need to employ technologies to substitute those engineering practices which result in emissions contributing to the death of our earth has arisen. One of those technologies is one that can sufficiently replace overdependence on laboratory activities where oxides of carbon and other toxins are released. Also, it is one technology that brings precision to other engineering activities especially earthwork design and construction thereby reducing to lower ebb the release of carbon oxides due to inexact utilization of materials during geotechnical practices. In this review, the use of artificial intelligence techniques in geotechnics has been explored as a precise technique through which geotechnical engineering works don’t impact on our planet due to precision. The intelligent learning algorithms of ANN, Fuzzy Logic, GEP, ANFIS, ANOVA and other nature-inspired algorithms have been reviewed as they are applied in the prediction of geotechnical and geoenvironmental problems and system. It is a complex exercise to conduct experimental protocols during the design and construction of earthwork infrastructures. Most times, such experimental exercises don’t meet the required condition for sustainable design and construction. At other times, certain errors as a result of experimental set up or human misjudgment may mar the accuracy of measurements and release unexpected emissions. The employment of the evolutionary learning methods has solved most of the lapses encountered in repeated laboratory measurements. So, in this review work, the relevant computational intelligent techniques employed at different times, under different laboratory protocols and utilizing different materials, have been presented as a comprehensive guide to future researchers in this innovative and evolving field of artificial intelligence. With this extensive review, a researcher would not have to look far to get a technical and state of the art guide in the utilization of various intelligent techniques that would enable engineering models in a more efficient, precise and more sustainable approach to forestall multiple practices that release carbon emissions into the environment