In the recent years, owners and construction management companies have shown an increasingly more interest in adopting approaches that result in enhanced quality and less risks, conflicts, and wastes on their projects despite potentially higher initial cost. Implementing advanced technology trends and incorporating more integrated methods of delivering projects have proven to be highly value-adding and forward-thinking approaches. The objective of this research was to evaluate the effectiveness of and the synergy between three of such trending concepts in the construction industry, namely, integrated project delivery (IPD), lean principles, and building information modeling (BIM) in terms of cost and schedule performance measures. Data analysis was conducted on 72 vertical projects through interviews and study of the published articles, reports, and case studies. Qualitative analysis was performed through grounded theory while quantitative analysis was implemented using univariate and multivariate analysis of variance tests on schedule performance and cost performance. Results of the grounded theory analysis summarize six crucial characteristics required for an effective coordination between IPD, lean construction, and BIM. Statistical analysis on different combination of these three components revealed considerable effectiveness in terms of schedule performance while the effect on cost performance was not as much significant. This study contributes to the body of knowledge and practice in the field of construction by demonstrating the cost and schedule benefits realized through the use of IPD, lean construction, and BIM and identifying their collective conceptual advantages.

Manual material handling tasks have the potential to be highly unsafe from an ergonomic viewpoint. Safety inspections to monitor body postures can help mitigate ergonomic risks of material handling. However, the real effect of awkward muscle movements, strains, and excessive forces that may result in an injury may not be identified by external cues. This paper evaluates the ability of surface electromyogram (EMG)-based systems together with machine learning algorithms to automatically detect body movements that may harm muscles in material handling. The analysis utilized a lifting equation developed by the U.S. National Institute for Occupational Safety and Health (NIOSH). This equation determines a Recommended Weight Limit, which suggests the maximum acceptable weight that a healthy worker can lift and carry, as well as a Lifting Index value to assess the risk extent. Four different machine learning models, namely Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Random Forest are developed to classify the risk assessments calculated based on the NIOSH lifting equation. The sensitivity of the models to various parameters is also evaluated to find the best performance using each algorithm. Results indicate that Decision Tree models have the potential to predict the risk level with close to 99.35% accuracy.

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