Probability Density Function for Predicting Productivity in Masonry Construction Based on the Compatibility of a Crew

During the different phases of a masonry project, contractors collect detailed information about the labor productivity of its workers and the factors that influence productivity. Information includes quantitative data such as hours, activities, and tasks, and qualitative data such as ratings and personality factors. Personality factors have been found to be a key aspect that influences the compatibility of a crew and the productivity in masonry construction. This paper proposes a mathematical framework to determine how the compatibility between the workers in a crew can be used to predict productivity. A standard method for quantifying personality is used to determine the compatibility of a crew and empirically define a probability density to predict productivity. The probability density determines, for a given compatibility, the average productivity for a crew. The most interesting part of this probability density is that it accounts for variations in the productivity, resulting from the interaction and the relationships between the workers in a crew. The proposed probability distribution can be used to make more realistic predictions, by calculating confidence intervals, of the productivity of masonry crews and to better estimate times of construction, avoid crew conflicts, and find practical ways to increase production

Compatibility of Personality and Productivity: An Analysis of the Relationship with Construction Crews

The labor productivity of a crew depends on how efficiently workers are used in the construction process. Skills, capabilities, resources, and even personality affect the efficiency of the workers and may have an impact on the productivity of their crew. This paper illustrates how the personality profiles of the workers in a crew can be used to determine the relationship between compatibility of personality and productivity. Masons working in eight live construction projects completed the big five of personality to indicate their personality traits. Based on the personality traits, the compatibility of the crews was calculated. Productivity at the task-level was measured to determine the performance of the crews. Various statistical analyses are performed to establish the relationship between compatibility and crew productivity and the true value of the coefficient (and its likeliness). The results indicate that there is a high positive correlation between compatibility of personality and productivity at the task-level (rs = 0.758). Results also indicate that in the worst case scenario, there is a moderate correlation between compatibility and productivity (rs > 0.3; probability: 0.728). The implications of the relationship for managing crews in construction projects is discussed

Does Compatibility of Personality Affect Productivity? Exploratory Study with Construction Crews

Crew productivity is a function of how efficiently labor is utilized in the construction process. However, previous research in construction has not comprehensively investigated the relationship between personality and crew productivity. This paper uses personality profiles to investigate a new fundamental concept, the relationship between compatibility of personality and crew productivity at the task level. Twenty-eight masons completed a revised questionnaire of the Big Five to indicate their personality. Personality scores were used to calculate compatibility in each of the 20 participating two-mason crews working on eight projects. Regression analysis was performed to establish the relationship between compatibility and crew productivity. Results show that that there is a high positive correlation between compatibility and crew productivity. Compatibility accounts for more than half of the predictable variance in productivity. This paper makes four major contributions: it proposes a new metric to measure compatibility of personality among workers in a crew; it reveals how personality factors affect productivity; it provides rigorous methods to analyze correlations (using confidence intervals and Bayesian inference) for construction experiments; and it provides theoretical contributions to advancing the theory of personality and productivity in construction projects

Predicting construction productivity with machine learning approaches

Machine learning (ML) is a purpose technology already starting to transform the global economy and has the potential to transform the construction industry with the use of data-driven solutions to improve the way projects are delivered. Unrealistic productivity predictions cause increased delivery cost and time. This study shows the application of supervised ML algorithms on a database including 1,977 productivity measures that were used to train, test, and validate the approach. Deep neural network (DNN), k-nearest neighbours (KNN), support vector machine (SVM), logistic regression, and Bayesian networks are used for predicting productivity by using a subjective measure (compatibility of personality), together with external and site conditions and other workforce characteristics. A case study of a masonry project is discussed to analyse and predict task productivity