Investigating the Relationship between the Production Characteristics and Quality of Concrete Produced in Aba, Abia State, Nigeria

Quality of concrete explains considerably the state of affairs involved in the production of concrete in a particular area. The antecedents of the production process to a greater extent therefore have a way of predicting the expected quality of a product. Hence, the degree of relationship between the quality of concrete and its production characteristics is determined in the study. Field work adopted in the study comprises activity sampling and ex post facto experimental designs. These two different research designs were applied to obtain data for performance assessment of activities involved in the production procedures and laboratory tests of concrete produced at various sites in the study area respectively. The corresponding data obtained in the field work are in ordinance and ratio scales of measurement. Regression analysis was used to establish a casual relationship between the two variables - performance level of the production characteristics (x) and the compressive strength (y) of the concrete at various sites in the study area. A model relationship of simple regression analysis for the dependent and independent variables is established. Finding reveals that the better the ranking of sites in compliance with the standard practice of production characteristics, the higher the compressive strength property of the concrete produced on site; hence the linear relationship. The coefficient of determination shows that 93% of changes in the strength property of concrete are caused by the production characteristics. Although the highest value of compressive strength obtained as 10.80 N/mm2 goes with the best state of affairs of the production characteristics in the study, it does not meet the minimum stipulated specification for the strength. Hence, other critical factors such as; aggregate type, and mix design should be considered for desired quality of concrete in the study area. Besides, enforcement of uniformity in production process as standard practice by all the firms should as a matter of urgency be implemented formally by the government in the state for improved quality of concrete in general.

Analysis of strength property and pore characteristics of Taihang limestone using X-ray computed tomography at high temperatures

Rising temperature will cause the changes of pore characteristics and strength property in rock. This research takes the limestone produced in Taihang Mountains as the research object, and performs high-temperature treatment within 25–1000 °C. The high-resolution X-ray computed tomography (CT) scanning test method is used to visually reconstruct the three-dimensional image of the sample, and obtain the spatial distribution status of the mesoscopic parameters of the bones, pores/cracks, etc. The results show that when the temperature exceeded 700 °C, the samples appeared milky white in appearance and as the temperature increased, the color gradually turned white, macroscopic cracks began to appear on the surface, while the meso-pores connected rapidly, reflecting a typical progressive destruction process from inside to outside. The change law of volume porosity with temperature has a consistent trend with that of the apparent morphology of the sample. Similarly, the mechanical test results suggest that 700 °C is also the turning temperature for strength deterioration and brittle-plastic transformation of sample. Based on the results of high-temperature test, CT test and mechanical test, there are enough evidences to show that, for the limestone sample, 700 °C is probably to be the mutation temperature of physical–mechanical behavior.

Comparative Studies of the Shear Strength Property of Two Members of the Fabaceae Family with Respect to Their Fibres

Aim: The shear strength of wood is a very significant parameter required in describing the potential of woods in making wooden structure. This study is aimed at comparing the shear strength of Pentaclethra macrophylla and Erythrophleum suaveolens with respect to their fibre. This is to determine the suitability of including Erythrophleum suaveolens in making traditional motars in order to reduce the demand load on Pentaclethra macrophylla. Methods: The heartwood samples of Pentaclethra macrophylla and Erythrophleum suaveolens for maceration were fixed in specimen bottles containing formalin-acetic-alcohol (FAA) in the ratio 90:5:5 to prevent fungal growth. The preparations involved cutting small clear samples of the heartwood of the two timber species of fabaceae family. The shear strength parallel to the grain test was conducted using a Hounsfield Tensometer. Results: Significant differences were recorded across the fibre characteristics of the two plant species. The share strength of the 25 wood samples from the two plants each fluctuate around 100 to 200 N/mm2. On the average, P. macrophylla recorded higher shear strength as compared to suaveolens however no significant difference was recorded between the means when tested for significant differences using independent sample t-test. Conclusion: Since the shear strength of E. suaveolens is comparable to that of P. macrophylla, it is therefore recommended its substitute for the manufacturing of wood based products where P. macrophylla has been in continual usage in order to relieve the pressure and demand on P. macrophylla.

An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strength

Shear strength is a crucial property of soils regarded as its intrinsic capacity to resist failure when forces act on the soil mass. This study proposes an advanced meta-leaner to discern the shear strength property and generate a reliable estimation of the ultimate shear strength of the soil. The proposed model is named as metaheuristic-optimized meta-ensemble learning model (MOMEM) and aims at helping geotechnical engineers accurately predict the parameter of interest. The MOMEM was established with the integration of the artificial electric field algorithm (AEFA) to dynamically blend the radial basis function neural network (RBFNN) and multivariate adaptive regression splines (MARS). In the framework of forming MOMEM, the AEFA consistently monitor the learning phases of the RBFNN and MARS in mining soil shear strength property through optimizing their controlling parameters, including neuron number, Gaussian spread, regularization coefficient, and kernel function parameter. Simultaneously, RBFNN and MARS are stacked via a linear combination method with dynamic weights optimized by the AEFA metaheuristic. The one-tail t test on 20 running times affirmed that with the greatest mean and standard deviation of RMSE (mean = 0.035 kg/cm2; Std. = 0.005 kg/cm2), MAE (mean = 0.026 kg/cm2; Std. = 0.004 kg/cm2), MAPE (mean = 7.9%; Std. = 1.72%), and R2 (mean = 0.826; Std. = 0.055), the MOMEM is significantly superior to other artificial intelligence-based methods. These analytical results indicate that MOMEM is an innovative tool for accurate calculating soil shear strength; thus, it provides geotechnical engineers with reliable figures to significantly increase soil-related engineering design.