Thesis (B.S.) -- Cagayan State University, 2025.

Concrete pouring is an important and labor-intensive task that affects productivity in construction projects. This study developed a multiple regression model to predict the productivity of concrete work in construction projects in Tuguegarao City. A thorough review of existing literature identified 25 factors affecting concrete work productivity, which were classified into four major categories: (1) construction design, planning and practices, (2) materials and equipment, (3) workforce, and (4) site conditions. Survey was administered to 90 construction professionals, and the Relative Importance Index (RII) method was used to determine the 10 most influential factors, with weather condition being the most influential. Data from 17 construction site observations were collected to create the regression model using the forward method in SPSS. The findings showed that the method of concrete pouring, the number of mixers, floor level, and number of workers were the key factors predicting productivity. The model explained 80.3% of the variation in productivity and had a Mean Absolute Percentage Error (MAPE) of 10.11%, indicating good accuracy. This model provides a helpful tool for construction professionals and companies to improve planning, predict productivity, and allocate resources more effectively. Future research should gather more data from different regions and regularly update the model to reflect new construction practices and technologies.

Keywords: concrete pouring, concrete works, productivity, multiple linear regression