Risk source-based constructability appraisal using supervised machine learning

Abstract Appraising a technical project's constructability is pivotal in its objectives' achievement, performance improvement, and collaborative lifecycle management. However, it has never been computationally integrated with risk analysis. This paper presents the construction, implementation and validation of a novel methodological and computational framework tackling such an integration for the first time, by treating it as a classification problem. Real projects' risk- and constructability class-related data was collected, and the risk elements' values were normalized and assigned to a general risk source checklist. Then, regularized stochastic gradient descent non-negative matrix factorization dealt with missing values and factorized the data into vectors. The latter were processed with sequential minimal optimization – employed to solve the soft-margin support vector machines for supervised machine learning classification – which was trained and validated through n-fold cross-validation. The result was a classification equation that predicts with high accuracy a project's constructability class, given its identified and assessed risk sources.