Failure of concrete structures leading to collapse of buildings has initiated various researches on the quality of construction materials. Collapse of buildings resulting to injuries, loss of lives and investments has been largely attributed to use of poor quality concrete ingredients. Information on the effect of silt and clay content and organic impurities present in building sand being supplied in Nairobi County and its environs as well as their effect to the compressive strength of concrete was lacking. The objective of this research was to establish level of silt, clay and organic impurities present in building sand and its effect on compressive strength of concrete. This paper presents the findings on the quality of building sand as sourced from eight supply points in Nairobi County and its environs and the effects of these sand impurities to the compressive strength of concrete. 27 sand samples were tested for silt and clay contents and organic impurities in accordance with BS 882 and ASTM C40 respectively after which 13 sand samples with varying level of impurities were selected for casting of concrete cubes. 150 mm × 150 mm × 150 mm concrete cubes were cast using concrete mix of 1:1.5:3:0.57 (cement:sand:coarse aggregates:water) and were tested for compressive strength at the age of 7, 14 and 28 days. The investigation used cement, coarse aggregates (crushed stones) and water of similar characteristics while sand used had varying levels of impurities and particle shapes and texture. The results of the investigations showed that 86.2% of the sand samples tested exceeded the allowable limit of silt and clay content while 77% exceeded the organic content limit. The level of silt and clay content ranged from 42% to 3.3% for while organic impurities ranged from 0.029 to 0.738 photometric ohms for the un- 256was generated predicting compressive strength varying levels of silt and clay impurities (SCI), and organic impurities (ORG) in sand. This implies that 44% of concrete's compressive strength is contributed by combination of silt and clay content and organic impurities in sand. Other factors such as particle shapes, texture, workability and mode of sand formation also play a key role in determination of concrete strength. It is concluded that sand found in Nairobi County and its environs contain silt and clay content and organic impurities that exceed the allowable limits and these impurities result in significant reduction in concrete's compressive strength. It is recommended that the concrete design mix should always consider the strength reduction due to presence of these impurities to ensure that target strength of the resultant concrete is achieved. Formulation of policies governing monitoring of quality of building sand in Kenya and other developed countries is recommended.

PurposeThe purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.Design/methodology/approachThe proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.FindingsThe findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.Research limitations/implicationsThe study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.Originality/valueThe study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.

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Flood management requires in-depth computational modelling through assessment of flood return period and river flow data in order to effectively analyze catchment response. The participatory geographic information system (PGIS) is a tool which is increasingly used for collecting data and decision making on environmental issues. This study sought to determine the return periods of major floods that happened in Narok Town, Kenya, using rainfall frequency analysis and PGIS. For this purpose, a number of statistical distribution functions were applied to daily rainfall data from two stations: Narok water supply (WS) station and Narok meteorological station (MS). The first station has a dataset of thirty years and the second one has a dataset of fifty-nine (59) years. The parameters obtained from the Kolmogorov–Smirnov (K–S) test and chi-square test helped to select the appropriate distribution. The best-fitted distribution for WS station were Gumbel L-moment, Pareto L-moment, and Weibull distribution for maximum one day, two days, and three days rainfall, respectively. However, the best-fitted distribution was found to be generalized extreme value L-moment, Gumbel and gamma distribution for maximum one day, two days, and three days, respectively for the meteorological station data. Each of the selected best-fitted distribution was used to compute the corresponding rainfall intensity for 5, 10, 25, 50, and 100 years return period, as well as the return period of the significant flood that happened in the town. The January 1993 flood was found to have a return period of six years, while the April 2013, March 2013, and April 2015 floods had a return period of one year each. This study helped to establish the return period of major flood events that occurred in Narok, and highlights the importance of population in disaster management. The study’s results would be useful in developing flood hazard maps of Narok Town for different return periods.

The demand for virgin aggregates for concrete production worldwide has been increasing. At the same time, there is an increase in the production of rubbles from construction-related activities. The residues are produced either due to leftovers or from the demolished structures. However, worldwide, the utilization of recycled concrete aggregate (RCA) material for structural concrete has been limited, often considered inferior due to distorted surfaces. The problems with RCA arise because it is a composite aggregate made of natural aggregate and cement, and adhered surface mortar is the discontinuities on the surface that arise from the production technique or the strength of the original concrete. The development of hydrated calcium silicate binder in the concrete matrix has been the critical desire of researchers to make better concrete material. This research focuses on the treatment of RCA from locally available rice husk ash with minimum pozzolanic cement. The objective was achieved by conducting tests on the properties of aggregates that include specific gravity and water absorption, aggregate crushing value (ACV), and aggregate impact value (AIV). The effectiveness of the treatment technique is assessed by the analysis of SEM imagery and XRD analysis of the microstructure cement paste around the RCA. At 20% pozzolan concentration, RCA treatment yields comparable specific gravities, water absorption, ACV, and AIV. Furthermore, the replacement of 5% rice husk ash (in 20% concentration) provided the optimal proportion of treatment for RCA, resulting in a reduction of ACV by 31.4%, AIV by 30.0%, and water absorption by 12.7% compared to the untreated RCA. XRD showed that calcite (CaCO₃), quartz, and portlandite phases were the majority in the untreated RCA. The study indicates that pozzolanic cement with 15% can be used with 5% RHA to produce RCA with characteristics almost similar to virgin aggregates. This research presents a consistent methodology to achieve modified RCA for application in construction.