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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]

Steel through-truss is a very common configuration for railroad and highway bridges. There is a large number of such spans in surface transportation infrastructure worldwide, especially in railway systems. In design and evaluation, these spans' in- and out-of-plane bending have been either omitted or approximated. A fuller understanding of this effect will be able to assist in reliably designing and assessing these structures for longevity and/or increasing allowable service load to facilitate economic developments. A full scale load test is performed in this paper on five such bridges of the Canadian National Railway (CN) using train load. The main truss elements prone to in- and out-of-plane bending are identified and strain-gauged including the hanger (L1U1 or L'1U'1). Out-of-plane bending is seen to produce more significant flexural stress than in-plane bending. Three-dimensional (3D) numerical simulation is also verified by physical tests and covers other uninstrumented members of the tested spans. These results are used to evaluate the accuracy of a new and simplifying two-dimensional (2D) analysis method for the most significantly bent vertical hanger out-of-plane. The 2D method is shown to capture a significant portion of the bending but still underestimate flexural stress. An empirical and hybrid approach is therefore developed and recommended to address the inadequately accounted out-of-plane bending for routine practice of design and evaluation. It is needed when resources for detailed 3D analysis are not readily available, and/or when a quick and reliable method is needed, e.g., for verification or calibration of another method. These results are also useful for stress range estimation for fatigue analysis, although fatigue is not a concern to these bridges and is therefore not specifically addressed in this paper. CN has adopted the recommended method and the other research findings in load-rating their existing through-truss bridges. [ABSTRACT FROM AUTHOR]

A system dynamics (SD) model was developed to identify the best road strategies for network-level maintenance and rehabilitation, considering technical, environmental, and economic grounds. This SD model uses triangulated research methods and data [e.g., interviews, policy analyses, and the VIZIR Pavement Distress Assessment Method (VPDAM)] to determine and allocate maintenance budgets in specific environmental and operational conditions. The system is tested via a case study at the Tunisian Radès Road Port (RRP). Baseline analyses of the simulation show the networked effects of key factors, including increased greenhouse gas (GHG) emissions, rehabilitation deficits, and climate change with reduced average pavement life. The SD model is calibrated and validated using data from RRP rehabilitation operations from 2020 until 2040. The model is coupled with an optimization module that recommends three optimal pavement solutions for the RRP and three pavement management systems (PMS) policies for improved sustainability. The model is based on sound technical principles, enhancing road function, sustainability, and cost savings. These findings contribute to the literature in PMS, SD modeling, sustainable development, and engineering management. [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]

In designing and evaluating roadway bridges in the surface transportation infrastructure, truck load is a major variable to account for. For daily operation, truck load also is a primary factor. Trucks on continuous spans were studied, with a focus on their behavior of longitudinal multiple presence in the same lane(s). Current US specifications include provisions for how to account for such multiple presence, and these provisions have evolved over the years. However, the basis for the provisions and their changes have not been well documented. An unprecedentedly large number of high-quality in-motion truck weight data were gathered in eight states across the US and used to investigate trucks' longitudinal multiple presence behavior. A new approach to analyzing each platoon of trucks recorded in traffic was developed for this purpose. Each random platoon's total load effect was identified, covering every truck in the platoon, to obtain reliable statistics of the maximum load effects. Results show that longitudinal multiple presence increases with truck volume and span length. Relevant current AASHTO provisions therefore were found to be conservative or overconservative. Accordingly, it was concluded that lower longitudinal multiple presence factor values for evaluation and design are more justifiable. These results may be used by bridge owners and engineers for realistic load rating and design, for more-uniform structural safety and cost effectiveness. This is consistent with the top two goals of the AASHTO Committee on Bridges and Structures' strategic plan for extending bridge service life and assessing bridge condition. [ABSTRACT FROM AUTHOR]

Low-density urban sprawl is reducing the quality of urbanization, especially in China. Improving the population aggregation degree is an essential means to solve the problem, and in this process, the central urban area plays a vital role. It is necessary to explain the population's spatiotemporal distribution mechanism and analyze the potential of population aggregation. The study took the central area of Tianjin, China, as an example, and the working framework focused on the comprehensive perspective of the urban built environment, which was divided into four dimensions (spatial construction intensity, spatial function characteristic, spatial accessibility, and spatial ecological quality). A geographically and temporally weighted regression (GTWR) model was first applied to determine the spatiotemporal relationship between the urban built environment and population-density distribution, and then, an urban-population aggregation-potential evaluation model was constructed through bivariate local spatial autocorrelation to analyze the potential for population aggregation. The results showed that spatial construction intensity and function characteristic indicators had the most significant impact intensity and were the main influencing factors, with spatial accessibility and spatial ecological quality indicators as subsidiary factors. Each index had the heterogeneity of influence in both time and space simultaneously, which was manifested in the spatiotemporal nonstationarity of the correlation coefficient. Most of the high-population aggregation-potential areas in the study were single-type high-potential areas, accounting for 37.78%, and were dominated by building density. Based on the mechanism and potential results of population spatiotemporal aggregation, the study provided new planning advice for Chinese cities to achieve better space utilization and sustainable urban development. [ABSTRACT FROM AUTHOR]

Residents of northern Tehran are well paid and have better access to urban services, whereas their southern counterparts are paid less and deprived. Land value also differs significantly between the north and south. However, it seems that having more apt objective indicators does not necessarily lead to an enhancement in subjective indices. The hypothesis is that "Justice is not perceived at a higher level among the dwellers of the higher-income quarters of Tehran compared with their counterparts in the lower-income quarters." To test this hypothesis, we attempted to define the criteria of justice, as perceived by Tehran citizens. According to previous studies, Tehran citizens generally perceive justice through four criteria: reduction of the gap between the poor and rich, government's assistance to the poor, law enforcement, and absence of corruption. Based on these criteria, justice is evaluated in Niavaran (a high-income quarter in northern Tehran with good access to urban services) and NematAbad (a low-income southern quarter with poor services) via a questionnaire (a randomly selected sample of 200 people). The results show that despite the existing objective differences between the infrastructures, services, and incomes, there is no significant difference between these quarters in terms of the perceived (subjective) justice (based on Mann–Whitney U test). The citizens in both quarters are dissatisfied with the status of all criteria. It is concluded that only physical strategies and enhancement in city services have not been accountable for the promotion and a sense of justice. Thus, the attention of planners should shift from physical indicators to mental indicators. [ABSTRACT FROM AUTHOR]