S.D. IYEKE

This study investigated the effect of partially replacing Ordinary Portland Cement (OPC) with eggshell powder (ESP) in concrete production as a sustainable approach to reduce cement consumption and utilize agricultural waste. The aim of the study was to evaluate the performance of concrete containing eggshell powder as a partial replacement for cement. The specific objectives were to determine the workability of concrete containing varying proportions of ESP, evaluate its compressive strength, assess its water absorption capacity as an indicator of durability, and identify the optimal ESP replacement level that yields the best concrete performance. The experimental study was conducted by preparing concrete mixes with 0%, 5%, 10%, 15%, and 20% replacement of cement with eggshell powder. Waste eggshells were collected, cleaned, dried, ground into fine powder, and sieved before use. Concrete cubes were cast and cured in water, after which several laboratory tests were performed. Workability of the fresh concrete was determined using the slump test, compressive strength was measured at 7, 14, and 28 days using a compression testing machine, and water absorption tests were carried out at 28 days to evaluate the durability-related properties of the hardened concrete.The results showed that workability slightly increased at 5% ESP replacement, indicating improved particle packing within the mix, but gradually decreased at higher replacement levels due to increased water demand of the fine ESP particles. The compressive strength of the concrete improved at moderate replacement levels, with the optimum strength obtained at 15% ESP replacement after 28 days of curing, while further increase in ESP content led to a reduction in strength. The water absorption values for all concrete mixes were below 10%, indicating that the inclusion of ESP did not adversely affect the durability of the concrete. Based on the findings, the study concluded that eggshell powder can effectively replace cement up to 15% without significantly compromising the essential properties of concrete. It is therefore recommended that ESP be considered as a sustainable partial cement replacement material in concrete production, particularly for applications such as pavements, floor screeds, and foundations, where environmentally friendly and cost-effective construction materials are desirable

The alarming rate of road traffic accident in the country (Nigeria) is among the most worrisome problems currently facing the nation. Sadly, Nigeria has earned the unenviable distinction of consistently leading all the nations of the world in high road traffic accident and high fatality rate. One of the best ways to understand the occurrence of road accident is to develop accident prediction models which are also standard practices in assessing and improving the safety of our roads. The aim of this study is to conduct a comprehensive evaluation of selected expert systems such as multiple linear regression and artificial neural
network for the modelling and prediction of road accident. The study area is Ugbowo-Lagos Road. A reconnaissance survey was done first to ascertain the geometric characteristic of the road which include; the chainage, the vertical and horizontal curve and the super elevation. Thereafter, secondary data which include road accident data was collected from Federal Road Safety Office at lucky way Benin City. To investigate the qualities of the secondary data, basic preliminary analysis techniques, namely;
outlier detection, homogeneity test, test of normality and autocorrelation test were done. While modelling and prediction of road accident was done with the aid of multiple linear regression and artificial neural networks. From the geometric characteristic of the road under study, it was observed that for a chainage of 11.5 to 13km, the vertical curve was 12.4% while the super elevation was 4.3%. Calculated Cronbach alpha value of 0.900 as observed in the reliability test revealed that the data are reliable and the computed goodness of fit statistics of reliability gave a maximum Guttman coefficient of 88.10% which further confirm the reliability of the data used. With a computed p-value greater than 0.05 for all the independent variables, the null hypothesis of the Dixon test was accepted and it was concluded that the accident data obtained from FRSC is devoid of outliers. In addition, with a centered VIF(Vehicle influence factor) < 10, it was concluded that there is the absence of multicollinearity between the dependent (NAC) and independent variables (NPIV, NPIJ, NPK, NVI). With a computed coefficient of determination (R 2) value of 0.9265, artificial neural network (ANN) was acclaimed better road accident prediction model compare to multiple linear regression model (MLRM) with a computed R2 value of 0.0617. The implication of this findings states that if the R2 value is
lesser than 0.0617 it is will not to work .