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.

This research aims to investigate the mechanical performance of compressed earth blocks (CEBs) stabilized by a combination of metakaolin-based geopolymer (MKG) and sugarcane molasses (SM), to remedy the limitations present in CEBs stabilized with MKG alone. Two schemes of stabilization were used. In the first, the optimum MKG content for stabilizing CEB was partially substituted with various percentages of SM (10% MKG + 0% SM, 8% MKG + 2% SM, 6% MKG + 4% SM, 4% MKG + 6% SM, 2% MKG + 8% SM). The second stabilization scheme consisted of fixing 5% MKG and varying SM from 2% to 8% (5% MKG + 0% SM, 5% MKG + 2% SM, 5% MKG + 4% SM, 5% MKG + 6% SM, 5% MKG + 8% SM). The mechanical properties of the CEBs stabilized with SM and MKG were analyzed in terms of compressive strength, dry density, and water absorption. The test results showed that the combination of MKG and SM for stabilizing CEBs was not as effective as MKG alone in increasing the compressive strength of CEBs. However, this combination solved the high porosity of CEBs stabilized with just MKG by increasing their dry density and decreasing their water absorption capacity. In terms of compressive strength and water absorption, the optimum values were obtained respectively with 5% MKG + 4% SM (4.163 MPa at 28 days) and 6% MKG + 4% SM (8.73% at 28 days). Therefore, the suggested innovative stabilization approach is suitable for improving the overall mechanical properties of CEBs and addressing the shortcomings of CEBs stabilized only with MKG. Doi: 10.28991/CEJ-2022-08-04-012 Full Text: PDF

Background: At the early phase of project development, highway engineering estimators seek to determine the duration of highway construction projects for the purpose of construction planning and administration. Thus, it is vital to study and analyze the estimation accuracy factors of highway construction project duration. In this regard, several studies have been conducted to identify and analyze the estimation accuracy factors of project duration in various ways to improve the estimation and management performance of all the contracting parties. However, very less effort has been devoted to evaluating the duration estimation accuracy factors in the case of the highway construction industry under fuzzy environment. Objective: This paper aims to analyze and prioritize the critical factors that potentially affect the duration estimation accuracy of the highway construction projects in Ethiopia under fuzzy environment. Methods: An extensive review and discussions with highway engineering experts were carried out to explore and identify the duration estimation accuracy factors. The study data collection process consists of two stages. The first stage is to conduct a questionnaire survey. Whereas, the second stage is to carry out the pair-wise comparison matrix to capture the imprecision and vagueness in subjective responses. Then, a λ-cut set method to reduce the initial list of factors and exploratory factor analysis was used to classify the reduced set of factors into smaller groups. Finally, a fuzzy hierarchy process algorithm with the use of triangular fuzzy numbers was presented for prioritizing critical factors. Results: A cut -off value, λ = 0.95, was verified which resulted in the identification of critical accuracy factors. Accordingly, 12 critical factors were opted and categorized as a cluster of similar items into 5 groups. Finally, the analytical results obtained from fuzzy AHP algorithm revealed that project complexity, project size, bridge type and complexity were found to be the four top-ranked factors based on the global priority weight. Conclusion: These factors must be a serious concern in estimating and administering the contract and the duration of highway construction projects at the early phases of project development so that the time deviation upon the completion of the project can be minimized.

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.