Modeling the Variability of Labor Productivity in Masonry Construction

This paper proposes a methodology to model the variability of masonry labor productivity. The theoretical basis of baseline productivity relied upon the analysis of 14 projects sharing similar exogenous conditions and being similar in scope, size of components, specifications, quality requirements and design features. The data were collected using standardized data collection procedures that focused on task-level labor productivity; specifically, the measurement of work accomplished by a single crew in a single shift. Analysis showed that when daily productivity values fall between the control limits, loss of productivity is within normal variation while daily productivity values falling above the upper control limit imply a loss of productivity that is due to the work environment factors as within the normal variation, and in particular to certain significant influential factors that can be cited during that day. These results could have significant implications for construction managers seeking to improve overall project performance

Prediction Model for Construction Cost and Duration in Jordan

Risk is mitigated in the course of reliable prediction. A probabilistic model is proposed to predict the risk effects on time and cost of construction projects. Project managers and consultants can use the model in estimating project cost and duration based on historic data. Statistical regression models and sample tests are developed using real data of 140 projects. The research objective is to develop a model to predict project cost and duration based on historic data of similar projects. The model result can be used by project managers in the planning phase to validate the schedule critical path time and project budget. Research methodology is steered per the following progression: i) Conduct nonparametric test for project cost and time performance. ii) Develop generic multiple-regression models to predict project cost and duration using historic performance data. iii) The percent prediction error is statistically analyzed; and found to be substantial; thus, iv) Custom multiple regression models are developed for each project type to obtain statistically reliable results. In conclusion, the 95% point estimation of error margin= ±0.035%. Therefore, at a probability of 95%, the proposed model predicts the project cost and duration with a precision of ±0.035% of the mean cost and time

Productivity Improvement of Pre-cast Concrete Installation

The production process of pre-cast concrete installation is analyzed to investigate possible ways for onsite productivity improvement. Although manufactured construction enjoys higher quality and productivity, it is observed that it suffers delays compared to site built construction. Delay causes and respective severity are analyzed for improvement. Firstly, the production process is investigated using the production delay model. Forty cycle data are used in the analysis. The comparative impact and severity are measured for five delay causes, namely: labor, environmental, management, equipment and material on overall system productivity. It is found via the production delay analysis that material, followed by equipment availability then labor were major contributors to system delay. Secondly, statistical analysis on the installation cycle time of three pre-cast component types is carried out, in order to insure whether the delay observed via the first step is attributed to variation of pre-cast pieces. The data used in step one above were not pertinent to product type; therefore, other 90 cycle data are utilized in the statistical analysis, which indicated high variability in cycle time due to product type. Improvement can be achieved through proper scheduling of project equipment and resources. In addition, improvement should target the reduction of installation cycle time variability due to product type