ANALYSIS ON EARTHWORK EQUIPMENTS’ PRODUCTIVITY USING ARTIFICIAL NEURAL NETWORK ON HIGHWAY PROJECTS IN ADDIS ABABA

Abstract

Equipment productivity is a key element in determining the successful completion of construction project for maintaining the scheduled construction activities especially in highway projects. The overall aim of the research is to analyze earthwork equipment productivity on highway projects in Addis Ababa. The survey method of data collection was employed to collect data from the stakeholders of highway projects in Addis Ababa. The collected data was analyzed using relative importance index (RII) and artificial neural network (RII). Accordingly, the research has identified three major factors which were the experience of the operator as human related factor, age of equipment as equipment related factors and interfacing of activities as management related factors. These factors were the common factors that affect the productivity of both excavator and truck while bucket capacity, height/depth of cut and horse power of the engine for the excavators only and size of truck and cycle time required for loading, hauling, damping and returning for the trucks only. After quantifying and measuring these factors, the mathematical forecasting model is developed for predicting the excavator and truck productivity of the future project with 0.000148 and 0.00116 of mean squared error (MSE) and 0.98131 and 0.96375 of correlation (R) for excavator and trucks respectively which indicate the high performance of the model to forecast the productivity. It also developed a model for excavator age with MSE = 0.00127 and R = 0.9647. Using the developed model sensitivity of factors were analyzed. The analysis indicates bucket capacity, type of soil, time taken due to interfering activity such as waiting time for trucks, and equipment age are identified as high sensitive influencing factors for excavator and cycle time for trucks productivity. For this reason, these factors especially the equipment age for excavators and cycle time for the truck should be the major and continual concern of the construction practitioners for proper management of the productivity of excavator and truck productivity. Generally, this research properly analyzed the factors affecting the productivity of excavator and truck for improving productivity and the forecasting model is developed for better duration estimation, scheduling, cash flow planning and resource optimization for highway projects in Addis Ababa