Building Informatics is a body of knowledge that uses the ICT computer system, digital systems, building information modeling, and state-of-the-art software in solving technical and management issues in building and construction fields. One of the modern methods used in data forecasting and modeling is Artificial Neural Networks, considering its advantage over traditional regression method. A data sample was taken of 1500 residential building projects' completion costs. Regression analysis was carried out and model validated with functionality and Jackknife re-sampling technique. 150 Questionnaires were used to capture data on factors influencing application of heuristics protocol for decisions in residential building construction projects and data samples were analyzed using severity index, ranking, and simple percentages. Analysis of data brought up some factors that influence effective application of heuristic protocol in solving decision problems in construction decision process. The linearity analysis was carried out on the model and results indicated high level of tolerance and -0.0876 lowest variation prediction quotients to 0.9878 highest variation quotients. Also, 0.069 regression model fitness coefficient (R-square) was generated with 0.9878 highest variation quotients with standard error of 0.045. The results data attests to the stability of the model generated and the model is flexible in accommodating new data and variables, thus, allows for continuous updating.
Full Text (PDF)