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.

Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide-ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.

Nanotechnology is one of the most common areas for current research and development in almost all technological fields. A significant factor is the synergistic benefit of nanoscale dimensions over larger scale alteration. Polymer nanoscience is the analysis and application of nanotechnology to polymer nanoparticle matrices, with nanoparticles described as those with at least one dimension of less than 100 nm. The use of polymer nanotechnology and nanocomposites in practical applications is a rapidly developing area. For making polymeric nanofibers from polymer melts and solutions, a spinning technique is used known as electrospinning. Electrospinning is an easy way to produce ultrafine fibers, which is nanosize. For its wide range of variety of spinning polymeric fibers, it is recommended, as well as producing fibers in nanosize accurately. The aim of this project is to use electrospinning to make nanoclay integrated polycaprolactone membranes. The effects of the nanoclay on morphology, thermal, and sorption behaviors of the electrospun membrane were further studied. The scope of this project work is that the electrospun nanocomposites are best studied for biomedical applications. Because of their influence over porosity, pore size, and fiber diameter, they make excellent scaffold materials.

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