Although industrialized construction methods, such as modular and manufactured buildings, have evolved over decades, core techniques used in prefabrication plants vary only slightly from those used in traditional site-built construction. With the focus of energy efficiency in modular construction, this research presents the development of a simulation, measurement, and optimization system for determining energy consumption (EC) in the manufacturing process of modular construction. The system uses the principles of a loosely coupled system operation to identify the nonvalue-added tasks and possible causes of low energy efficiency. The system includes value stream mapping (VSM), discrete optimization, and system dynamics (SD) for the demonstration and calculation of EC in modular construction. The benefits of implementing this system include the quantity analysis of possible waste factors, reductions in the EC, decrease of energy cost, and increase of profit. In addition, the research reveals the relationships between relocatable workers, energy efficiency, and operation flexibility. This system should provide superior performance over existing operation evaluation systems, enabling decision-making based on reliable quantities and delivery of true EC models to facility construction practitioners.

Understanding the relationship between concrete temperature development and field curing time helps to control material quality, improve construction efficiency, and enhance research on concrete design. However, it is difficult to precisely predict temperature trends when placing concrete because there are many influencing factors and uncontrollable ambient variables in the curing process. To forecast the short-term temperature trends reliably and automatically, this research proposes a temperature measurement and quality prediction (TMQP) system to proactively evaluate the development trajectory of concrete quality and the temperature changes at the center and surface of the cross section of concrete structural members. The TMQP system includes radio-frequency identification (RFID) temperature sensors for recording the temperature data and Big Data analytics (BDA) combined with the machine-learning method of classification and regression tree (CART) for measuring and predicting of temperature development. The results indicate that the system has over 98% reliability on the correlation coefficients between the predicted temperatures and actual temperatures based on 240 h of continuous experiments and 190 h of documented data. This entire research design is applicable to various concrete construction projects and sheds light on how BDA and machine learning can help construction engineers and managers to control concrete curing and take preventive measures to avoid concrete surface cracks.

The purpose of this research is to study the vulnerability of construction enterprises’ innovation capabilities (CEIC) and their respective primary influencing factors. This paper proposed a vulnerability system framework of CEIC, designed two comprehensive assessments for analysis, namely the entropy and set pair analysis method (E-SPA) and the principle cluster analysis and SPA method (P-SPA), and compared grades to verify the vulnerability assessments. Further, the paper quantitatively assessed the major influencing factors in facilitating management, reducing vulnerability, and improving the ability of construction enterprises to respond to changes in the construction industry. The results showed that vulnerability could be effectively and systematically evaluated using E-SPA. However, managing or reducing entrepreneurial sensitivity and improving the ability to respond was critical to supporting sustainable CEIC. The case studies included in this paper suggested that in ensuring sustainable CEIC, companies should concentrate on highly educated human resources, R&D investments, intellectual property related innovations, and government support. This research provided a practical framework and established a sustainable strategy for companies to manage their vulnerability in developing innovation capability. In addition, this research presented an innovative and effective way to quantitatively analyze vulnerability which offered a foundation to signify a new paradigm shift in construction sustainable development.

Over the years, 33 incidents of offshore blowout during drilling have caused significant damage to life and property. This research suggests that field engineers could prevent offshore blowout using timely detection of the impending danger, a.k.a. a kick, using big data analysis tools. In this study, the objective is to develop an algorithm that can detect a kick and display the result without any human intervention. The research purpose is to achieve immediate response time in the detection of kicks to deploy control measures. The contribution of the study is an inexpensive and generalizable automated mud-logging system for the analysis and detection of kicks in underwater drilling projects. The research innovatively implemented the genetic wavelet neural network method to monitor and predict kicks in real time. The developed kick-detection system is able to capture and represent complex input and output relationships of the primary and secondary indicators of the kick to detect any anomalies and display warning messages. Field engineers then use these messages to confirm the possible occurrence of a kick and inform the driller to perform the appropriate control. This algorithm has the potential to be a lifesaving solution for engineers and managers working on offshore projects pile drilling for bridges or waterworks.

Integrated project delivery (IPD) is getting increasing attention in construction project administration of the industry. Among multiple factors that could influence successful delivery, collaboration, profitability, financial incentives, and shared responsibilities are correlated with each other and have critical importance in the contractual mechanisms. There is currently inefficiency in the studies to understand and evaluate the effectiveness of contract administration in shared obligation and achievement in the United States. The objective of this paper is to present a study of the contract provisions of the shared responsibilities and their relationship with financial incentives in IPD projects. An evaluation has been undertaken to define this relationship to meet the intricacies and challenges of the construction industries. Case studies of IPD from different construction projects are discussed. The study reveals that symmetrical alignment of shared responsibilities, financial incentives, and good communication approach is the main reason for IPD project success.