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Construction and demolition waste (C&DW) contribute to approximately 30% of the total waste generation worldwide, by which heterogeneous ecological impacts, such as resource depletion, global warming, and land degradation, are engendered. Despite ongoing research efforts to minimize construction waste via the Building Information Modeling (BIM)-aided design, there is a paucity of research on integrating BIM in demolition waste management (DWM). This study investigates prominent barriers and future research directions toward the wider adoption of BIM in C&DWM by conducting a systematic literature review. First, this study identifies the barriers that hinder the implementation of C&DWM in Australia; then, it explores the benefits and challenges of leveraging BIM applications for C&DWM. The findings suggest that, for existing buildings without up-to-date design drawings, it is imperative to improve the accuracy of data capturing and object recognition techniques to overcome the bottlenecks of BIM-DWM integration. Moreover, the development of regional-oriented material banks and their harmonization with life cycle assessment databases can extend the potential of BIM-based sustainability analysis, making it applicable to the DWM domain. This study proposes a research agenda on tackling these challenges to realize BIM’s full potential in facilitating DWM.

Three-dimensional (3D) printing technology is increasingly playing an essential role in the customization of prefabricated buildings. To demonstrate its high-quality, cost, and time-saving...

Because of the complex and fine structure, visualization of the heart still remains a challenging task, which makes it an active research topic. In this paper, we present a visualization system for medical data, which takes advantage of the recent graphics processing unit (GPU) and can provide real-time cardiac visualization. This work focuses on investigating the anatomical structure visualization of the human heart, which is fundamental to the cardiac visualization, medical training and diagnosis assistance. Several state-of-the-art cardiac visualization methods are integrated into the proposed system and a task specified visualization method is proposed. In addition, auxiliary tools are provided to generate user specified visualization results. The contributions of our work lie in two-fold: for doctors and medical staff, the system can provide task specified visualization with interactive visualization tools; for researchers, the proposed platform can serve as a baseline for comparing different rendering methods and can easily incorporate new rendering methods. Experimental results show that the proposed system can provide favorable cardiac visualization results in terms of both effectiveness and efficiency.

In this study, we develop an adaptive-critic-based controller to steer an agile missile that has a constraint on the minimum flight Mach number from various initial Mach numbers to a given final Mach number in minimum time while completely reversing its flightpath angle. This class of bounded state space, free final time problems is very difficult to solve due to discontinuities in costates at the constraint boundaries. We use a two-neural-network structure called "adaptive critic" in this study to carry out the optimization process. This structure obtains an optimal controller through solving optimal control-related equations resulting from a Hamiltonian formulation. Detailed derivations of equations and conditions on the constraint boundary are provided. For numerical experiments, we consider vertical plane scenarios. Flight Mach number and the flightpath angle are the states and the aerodynamic angle of attack is treated as the control. Numerical results bring out some attractive features of the adaptive critic approach and show that this formulation works very well in guiding the missile to its final conditions for this state constrained optimization problem from an envelope of initial conditions.

A neural-network-based synthesis of an optimal midcourse guidance law is presented in this study. We use a set of two neural networks; the first network called a “critic” outputs the Lagrange's multipliers arising in an optimal control formulation and second network, called an “action” network, outputs the optimal guidance/control. The system equations, the optimality conditions, the costate equations are used in conjunction with the network outputs to provide the targets for the neural networks. When the critic and action network are mutually consistent, the output of the action network yields optimal guidance/control. Numerical results for a number of scenarios show that the network performance is excellent. Corroboration for optimality is provided by comparisons of the numerical solutions using a shooting method for a number of scenarios.

Transfer function design is a challenging issue of volume visualization. Some simulation and medical applications which require visualization often produce at least two values per sampling point in the so called multi-modal data, which makes transfer function design even more complex. In this paper, we present a novel visualization method for assisting the assessment of electrophysiological simulation results. The main idea of the proposed method is that the anatomical data of the heart is added as background information to enhance the illustration results of the tissue with electrophysiological behaviour. A improved context-preserving model is proposed to enhance the boundary information of different tissue. The two modalities data of both tissue with electrophysiological simulation and the other tissues are rendering by the proposed fusion rendering method with user friendly interactive facilities. Compared to conventional visualization method, our method can not only provide rendering result of tissue with electrophysiological simulation, but also can provide the spatial information of the given tissue in the heart.

We investigate the use of an 'adaptive critic' based controller to steer an agile missile with a constraint on the angle of attack from various initial Mach numbers to a given final Mach number in minimum time while completely reversing its flightpath angle. We use neural networks with a two-network structure called 'adaptive critic' to carry out the optimization process. This structure obtains an optimal controller through solving Hamiltonian equations. This approach needs no external training; each network along with the optimality equations generates the output for the other network. When the outputs are mutually consistent, the controller output is optimal. Though the networks are trained off-line, the resulting control is a feedback control.