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In this research work, the levernberg Marquardt back propagation neural network was adequately trained to understand the relationship between the 28th day compressive strength values of hydrated lime cement concrete and their corresponding mix ratios with respect to curing age. Data used for the study were generated experimentally. A total of a hundred and fourteen (114) training data set were presented to the network. Eighty (80) of these were used for training the network, seventeen (17) were used for validation, and another seventeen (17) were used for testing the network's performance. Six (6) data set were left out and later used to test the adequacy of the network predictions. The outcome of results of the created network was close to that of the experimental efforts. The lowest and highest correlation coefficient recorded for all data samples used for developing the network were 0.901 and 0.984 for the test and training samples respectively. These values were close to 1. T-value obtained from the adequacy test carried out between experimental and model generated data was 1.437. This is less than 2.064, which is the T values from statistical table at 95% confidence limit. These results proved that the network made reliable predictions. Maximum compressive strength achieved from experimental works was 30.83N/mm2 at a water-cement ratio of 0.562 and a percentage replacement of ordinary portland cement with hydrated lime of 18.75%. Generally, for hydrated lime to be used in making structural concrete, ordinary portland cement percentage replacement with hydrated lime must not be up to 30%. With the use of the developed artificial neural network model, mix design procedure for hydrated lime cement concrete can be carried out with lesser time and energy requirements, when compared to the traditional method. This is because, the need to prepare trial mixes that will be cured, and tested in the laboratory, will no longer be required.

In this study, municipal plastic waste is used in producing paving blocks. Binders in the form of melted waste plastic bottles (Polyethelene Terephthalate (PET)) and water sachets (High Density Polyethylene (HDPE) with river sand, were used in making the blocks. Mix ratios; 1:1, 1:1.5 and 1:2 of sand-plastic waste were considered. Sand-cement mixes were adopted as the controls. 230mm x 140mm x 55mm blocks were cast, cured in water at ambient temperature and tested for 72-hours for water absorption and 21 days compressive strength. 3 specimen were prepared for each mixture. Sand-HDPE mix produced stronger blocks than sand-PET and sand-cement mixes. Topmost compressive strength of 17N/mm2 was generated from sand-HDPE mix of 1:2. Sand-PET blocks should be avoided since they generate very low strengths. Sand-plastic waste blocks melt faster at higher temperatures. Therefore, plastic paving blocks can only be used for light load pavements not subjected to high temperature.

In this study, a multivariate regression model for predicting the 28days flexural strength of lime-cement concrete prototype beam was developed. The response function is a multivariate function of the proportions of the component materials of concrete. A total of twenty mix ratios, consisting of water, Portland cement, hydrated lime, river sand and granite chipping were used in the prediction process. The first ten mix ratios were used for model development while the remaining ten mix ratios were used as check points for model validation. The model developed was tested for adequacy at 95% level of confidence using the t-statistic. Calculated t-value was -1.3342 and this was less than the critical t-value of 2.2622. Thus, the model was found to be adequate. An average percentage difference of 14.303% was observed between the model prediction and the experimental values. A visual basic program using the Visual studio 2015 software was developed based on the regression model. It was invoked to quicken the process of selecting the mix ratios of the component materials corresponding to any desired flexural strength value that falls within the region of experimentation and vice versa Keywords— Concrete , flexural strength, multivariate regression model, response function.

Parametric uncertainties should always be considered when setting design criteria in order to ensure safe and cost effective design of engineered structures. This paper presents the results of the reliability assessment of a fully laterally restrained steel floor I-beam to Eurocode 3 design rules. The failure modes considered are bending, shear and deflection. These were solved to obtain reliability indices using first order reliability method coded in MATLAB environment. Parametric sensitivity analyses were carried out at varying values of the design parameters to show their relative contributions to the safety of the beam. It was seen that reliability indices generally decreased with an increase in load ratio, imposed load, beam span in bending, shear stress and deflection respectively. In addition, increasing the beam span beyond 10 m, load ratio above 1.4 and imposed load beyond 30 kN/m made the beam fail as these parameters gave negative reliability indices. For failure in deflection, reliability index rose with an increase in the radius of gyration and overall depth of the beam section accordingly. Furthermore, the reliability index surged as the thickness of the web increased when taking into account, shear failure. The results of the analysis showed that the steel beam is very safe in shear and at some load ratios and imposed loads for failure in bending and deflection respectively. The average values of reliability indices obtained for load ratios ranging from 1.0 to 1.4 fell from 3.017 to 3.457 for all failure mode studied. These values are within the recommended reliability indices by the Joint Committee on Structural Safety for structure with moderate failure consequences and beams in flexure.