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Over the last 30 years, the last planner system (LPS), as a Lean-driven production planning and control tool, has consistently demonstrated its capability to improve planning reliability and overall performance in construction projects. Recent research provides evidence suggesting that the LPS ability to do good to projects has not reached its full potential yet. In fact, it has been observed that by identifying the missing social mechanisms in its implementation process and managing more effectively the complex socio-technical nature of the LPS, its implementation effectiveness is improved. However, commonly used research approaches, such as the case study method, have been criticized for lacking scientific control, replicability, and external validity. Alternatively, immersive virtual reality (IVR)-based simulation games have the methodological capability to enable the study of the social mechanisms that the LPS engenders when implemented in projects, by providing a highly controlled, reproducible, and ecologically valid experimental environment. However, how to leverage effectively IVR-based gaming technology to study the social mechanisms that the LPS engenders in construction organizations is not well understood. In order to bridge this gap, this paper established the conceptual and technical foundations to set IVR-based experimental environments and developed an IVR prototype encompassing nonlean and LPS-based rounds. The evaluation of the IVR prototype involved both quantitative and qualitative research approaches, focusing on its functionality, user-perceived presence and usability, task performance, and lean expert feedback. The results demonstrated the prototype's capability to create a controlled experimental environment while offering participants a user-friendly, immersive, and realistic LPS simulation experience. The contributions of this study are twofold: (1) the addition of a new method to the research methodology toolbox in construction engineering and management; and (2) the generation of design insights to develop IVR prototypes for research in construction engineering and management, and preliminary evidence for its ecological validity. [ABSTRACT FROM AUTHOR]

The interface bond strength between carbon fiber-reinforced polymer (CFRP) layers and concrete is a crucial metric for determining the mechanical properties of CFRP-reinforced concrete. This bond strength is essential for evaluating CFRP-reinforced concrete's performance and ensuring the materials' structural integrity. A database was established using the experimental data in the literature to evaluate the interface bond strength. This database comprised 360 groups of different conditions test results of CFRP-reinforced concrete, which were used to create a prediction model using an artificial neural network. The database was randomly divided into two data sets: 310 groups were used for training the neural network model and 50 for simulated prediction. A three-layer artificial neural network model was trained using the backpropagation algorithm, which is widely used in artificial neural networks. The model's input layer considered seven parameters, including the type of CFRP layer, surface form, CFRP layer thickness, anchorage length, failure mode, concrete compressive strength, and normalized concrete cover thickness. These parameters were selected based on their known influence on the interface bond strength between the CFRP layers and concrete. The output layer of the model represented the interface bond strength between the CFRP layers and concrete. The model's results indicated that the backpropagation (BP) neural network model had strong capability of prediction and generalization. The predicting error was minimal, a crucial aspect of the model's accuracy. Further, this approach allows for integrating many factors that influence the interface bond strength between the CFRP layers and concrete, providing accurate predictions of the bond strength. It can be used as a valuable tool for evaluating the performance of CFRP-reinforced concrete. This research develops an accurate method to predict the bond strength between CFRP layers and concrete using artificial neural networks. A strong bond is crucial for the structural integrity of concrete reinforced with CFRP. The neural network model considers factors like the type and thickness of CFRP used, how the concrete surface is prepared, and the concrete's strength. Engineers can use this neural network tool to evaluate how well CFRP will reinforce specific concrete mixtures and structures before construction. This allows structures to be designed and built with optimal, cost-effective use of CFRP to reinforce concrete in applications like bridges and buildings. The neural network approach integrates many technological and material factors into one predictive model, providing a useful evaluation method for the construction industry. [ABSTRACT FROM AUTHOR]

Highway traffic loads and environmental conditions, including temperature and moisture at the layer interface, could cause debonding or delamination between adjacent asphalt pavement layers in tensile, shear, or mixed-mode failures. Thus, studying the tensile and shear strengths of interface bonding is crucial to maintaining durable and functional pavement structures. The main objective of this research is to evaluate and estimate the interface bonding strength (IBS) between asphalt pavement layers under the mixed shear-tension loading mode. To this end, an experimental, statistical, and machine-learning (ML) approach was adopted. A total of 164 double-layered hot-mix asphalt specimens consisting of hot-mix asphalt AC-13 in the upper layer and AC-20 in the lower layer were tested via a direct tensile device with a supplementary shear fixture. The effects of test temperature, shear stress, and tack coat application rate on the IBS were considered. The results revealed that with increasing tack coat dosage, the IBS peaked at 0.8 kg/m2 and was subsequently decreased. It was also found that the IBS was very sensitive to temperature changes and heavily dependent on shear stress at elevated temperatures. On the other hand, with increasing shear stress from 0 to 0.20 MPa, the IBS at temperatures of 5°C, 20°C, and 35°C declined by 10.96%, 61.85%, and 83.16%, respectively. Two prediction models for the IBS based on the conventional statistical models of multiple linear regression (MLR) and nonlinear regression were successfully developed. However, the nonlinear model outperformed with a better prediction accuracy of 24.2% compared with linear regression (R2=71.8%). Finally, a highly accurate feed-forward back-propagation (FFBP) artificial neural network (ANN) model was developed to predict and form a relationship between the IBS and independent variables with an extremely low margin of error. It was revealed that the developed FFBP-ANN model could capture 99% of the measured data. Finally, a comparative analysis demonstrated that the developed FFBP-ANN model was superior to regression modeling in terms of predicting the IBS. [ABSTRACT FROM AUTHOR]