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Building information modelling (BIM) has been adopted in the construction industry. The success of BIM implementation relies on the accurate building information stored in BIM models. However, building information in BIM models can be inaccurate, out-of-date, or missing in real-world projects. 3D laser scanning has been leveraged to capture the accurate as-is conditions of buildings and create as-is BIM models of buildings; this is known as the scan-to-BIM process. Although industry practitioners and researchers have implemented and studied the scan-to-BIM process, there is no framework that systematically defines and discusses the key steps and considerations in the process. This study proposes an application-oriented framework for scan-to-BIM, which describes the four major steps of a scan-to-BIM process and their relationships. The framework is oriented towards the specific BIM application to be implemented using the created as-is BIM, and includes four steps: (1) identification of information requirements, (2) determination of required scan data quality, (3) scan data acquisition, and (4) as-is BIM reconstruction. Two illustrative examples are provided to demonstrate the feasibility of the proposed scan-to-BIM framework. Furthermore, future research directions within the scan-to-BIM framework are suggested.

Precast concrete elements are widely adopted and the performance of precast structures is relying on the quality of connections between adjacent elements. For reinforced precast concrete elements, rebar positions are important for the overall structural performance, however, they are usually manually inspected. This study develops a technique for automated position estimation of rebars on reinforced precast concrete elements using colored laser scan data. A novel mixed pixel filter is developed to remove mixed pixels from the raw scan data based on both distance and color difference. A one-class classifier is used for extracting rebars from all the data based on both geometric and color features of points. Furthermore, a novel rebar recognition algorithm is developed to recognize individual rebars based on two newly defined metrics. Experiments on two reinforced precast concrete bridge deck panels were conducted and showed that the proposed technique can accurately and efficiently estimate rebar positions. C 2017 Computer-Aided Civil and Infrastructure Engineering.

Keywords:NGBM(1,1) model Optimal algorithm Forecasting Qualified discharge rate of industrial wastewater a b s t r a c tThe Nonlinear Grey Bernoulli Model NGBM(1,1) performs well in the simulation and forecasting of series having non-linear variations. To improve the simulation and forecasting accuracy, the parameters optimization of an NGBM(1,1) model is formulated as a combinatorial optimization problem and is solved collectively using LINGO (an Operational Research software) in this paper. The optimized result has been verified by a numerical example of a fluctuating sequence and a case study of opto-electronics industry in Taiwan. Comparisons of the obtained simulation results from the optimized combinatorial NGBM(1,1) model with the traditional one demonstrates that the optimal algorithm is a good alternative for parameters optimization of the NGBM(1,1) model. The optimized NGBM(1,1) model is used to simulate and forecast the annual qualified discharge rate of industrial wastewater in 31 provinces of China for the period from 2001 to 2011. The modeling results can assist the government in developing future policies regarding environmental management.

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Since tremendous resources are consumed in the architecture, engineering, and construction (AEC) industry, the sustainability and efficiency in this field have received increasing concern in the past few decades. With the advent and development of computational tools and information technologies, structural optimization based on mathematical computation has become one of the most commonly used methods for the sustainable and efficient design in the field of civil engineering. However, despite the wide attention of researchers, there has not been a critical review of the recent research progresses on structural optimization yet. Therefore, the main objective of this paper is to comprehensively review the previous research on structural optimization, provide a thorough analysis on the optimization objectives and their temporal and spatial trends, optimization process, and summarize the current research limitations and recommendations of future work. The paper first introduces the significance of sustainability and efficiency in the AEC industry as well as the background of this review work. Then, relevant articles are retrieved and selected, followed by a statistical analysis of the selected articles. Thereafter, the selected articles are analyzed regarding the optimization objectives and their temporal and spatial trends. The four major steps in the structural optimization process, including structural analysis and modelling, formulation of optimization problems, optimization techniques, and computational tools and design platforms, are also reviewed and discussed in detail based on the collected articles. Finally, research gaps of the current works and potential directions of future works are proposed. This paper critically reviews the achievements and limitations of the current research on structural optimization, which provide guidelines for future research on structural optimization in the field of civil engineering.

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