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Subsidy incentive programs for accelerating the replacement of construction equipment are widely considered effective for reducing emissions from construction equipment. However, there are no known effective models for determining the optimal subsidy levels for voluntary early replacement of in-use high-emission construction equipment. In this study, a novel quantitative model is developed to address this problem by integrating emission reduction targets and life cycle cost of construction equipment to calculate the reduced service life of an item of construction for early replacement. The economic life of construction equipment and its corresponding equivalent uniform annual cost (EUAC) can be determined by using a traditional life cycle cost analysis model. Optimal subsidy levels are obtained on the basis that the subsidized EUAC of a piece of construction equipment over its shortened service life is not more than that over its normal economic life. The applicability of the proposed model is demonstrated through a case study of a crawler crane used in Hong Kong, whereby the subsidy levels for accelerating its replacement are determined. An interview with the contractor's equipment manager responsible for the crawler crane is conducted to validate the findings of the model. It is found that the optimal subsidy levels determined by the model can accelerate the replacement of construction equipment and achieve the goal of reducing emissions from construction equipment. Replacing construction equipment earlier than its economic life is a plausible strategy for emissions reduction but places a heavy financial burden on contractors if there is no financial compensation. This study shows that the subsidy levels determined by the proposed model can adequately compensate for the extra costs incurred from early replacement and would therefore not financially discourage contractors. This model also shows that the cost-effectiveness of subsidies increases as emission reduction targets are set at higher levels. [ABSTRACT FROM AUTHOR]

Carbon reduction is nowadays part of the sustainable development initiative worldwide, and promoting the use of biodiesel as a substitute for conventional diesel in non-road mobile machineries (NRMMs) used in the construction sector is a direct and effective way to reduce carbon emissions in this energy-intensive industry from a lifecycle perspective. In addition, the production and use of biodiesel made from waste cooking oil provide a solution to environmental issues and waste management, achieving a circular economy. Nevertheless, biodiesel usage in NRMMs in Hong Kong is largely limited to public works and the current biodiesel promotion and regulation measures are inadequate. This study aims to review and analyse biodiesel policies and promote the use of biodiesel in Hong Kong for low-carbon construction. Incentive and mandatory policies applicable to NRMMs are reviewed and analysed for two stages: biodiesel production and supply, and biodiesel consumption. Recommendations on biodiesel policies for the construction industry in Hong Kong are made accordingly, including expanding biodiesel production through incentives, and encouraging contractors to use biodiesel in private projects. A lifecycle analysis is then conducted to quantify the carbon reduction capability of using biodiesel. It is estimated that a lifecycle reduction of 3.2 × 104 tCO 2 e, i.e., a 4.5% decrease could be achieved by using B5 waste cooking oil biodiesel instead of diesel in the construction industry of Hong Kong. This study reviews and analyses biodiesel policies clearly and critically, paving the way for the establishment of feasible and effective regulations for promoting biodiesel use for low-carbon construction. [Display omitted] • Use of biodiesel in NRMMs to promote low-carbon construction and relieve the burden of disposing WCO for circular economy. • An in-depth review and critical analysis of biodiesel incentive and mandatory policies, for the two stages of biodiesel production and supply, and biodiesel consumption. • A lifecycle reduction of 3.2 × 104 tCO 2 e emissions can be achieved annually by using B5 WCO biodiesel in Hong Kong's construction industry, i.e., a 4.5% decrease. [ABSTRACT FROM AUTHOR]

Subsidies can incentivize contractors to replace old high-emitting construction equipment, but may put a financial burden on the government. A combination of fee and subsidy policy is regarded as cost-effective, but limited research was conducted on construction equipment. For this, the study sets up different scenarios to simulate the policies influencing contractors' selection of construction equipment. It also compares emissions reduction and government cost under different policy scenarios. The results of the earthwork case study in a Hong Kong public project show that the combined policy of fee and subsidy can achieve the same emission reductions of particulate matter (19.52 kg) and nitrogen oxide (83.78 kg) at a lower subsidy level (HK$3967) than the subsidy-only policy (HK$7934). The reduction of particulate matter and nitrogen oxide emissions is 0.59 kg and 68.45 kg through the fee-only policy, which is less than the combined policy. The sensitivity analysis results indicate that the contractor's budget can influence the government cost, and the different level of subsidy and fee can influence the emissions reductions. It is suggested that the implementation strategy of the combined policy should consider the budgets of contractors and governments. The proposed methods and findings can provide decision-making references for reducing emissions from construction equipment in different economies. [Display omitted] • Promote the use of low-emitting construction equipment through combined policies. • Model how fee and subsidy policies affect contractors in equipment selection. • Compare impact of four policy scenarios on emission reductions and government cost. • Suggest on fee and subsidy policies for construction equipment emissions reduction. [ABSTRACT FROM AUTHOR]

• A responsibility-sharing subsidy policy is proposed. • Responsibility for emission reduction is assigned by maximizing cost-effectiveness. • Subsidy level is determined by minimizing the overall cost of emission reduction. • In the subsidy policy contractors adopt optimal emission reduction strategies. • The responsibility-sharing subsidy policy offers contractors some flexibility. Subsidy policies to encourage contractors to replace or retrofit their in-use construction equipment is an accepted and effective way to mitigate emissions generated by construction equipment. However, existing subsidy policies do not consider the emission reduction responsibility of contractors as emitters, which causes inequity problems and fails to motivate contractors to innovate more cost-effective emission reduction strategies. This study therefore proposes a responsibility-sharing subsidy policy whereby the subsidy level is determined and the responsibility of contractors is assigned in proportion to emission reductions by the attaining of minimum overall cost per ton of emissions equivalent reduced. The case of a Hong Kong contractor's fleet of excavators is used to demonstrate the application of the proposed subsidy policy. In this case, a subsidy level in the range of HK$14,800 to HK$16,096 per ton of emissions equivalent reduced is recommended, with the responsibility of emissions reduction borne by the government from 92% to 100% and the contractor from 0% to 8%. The corresponding cost-effectiveness of the designed subsidy policy is about HK$16,096 per ton of emissions equivalent reduced, which is reasonable and acceptable. This study also finds that by assigning proper emissions reduction responsibility to contractors, governments can encourage contractors to adopt optimal strategies of replacing and retrofitting construction equipment to reduce emissions with minimum overall cost per ton of emissions equivalent reduced. The proposed subsidy policy for reducing construction equipment emissions could enable informed decisions of governments, especially for those facing a pressing demand for improving the cost-effectiveness of subsidy policy for reducing emissions from construction equipment. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a comparative study on the applications of general regression neural network (GRNN) models and conventional Box–Jenkins time series models to predict the maintenance cost of construction equipment. The comparison is based on the generic time series analysis assumption that time-sequenced observations have serial correlations within the time series and cross correlations with the explanatory time series. Both GRNN and Box–Jenkins time series models can describe the behavior and predict the maintenance costs of different equipment categories and fleets with an acceptable level of accuracy. Forecasting with multivariate GRNN models was improved significantly after incorporating parallel fuel consumption data as an explanatory time series. An accurate forecasting of equipment maintenance cost into the future can facilitate decision support tasks such as equipment budget and resource planning, equipment replacement, and determining the internal rate of charge on equipment use. [Copyright &y& Elsevier]

Abstract: The occurrences of construction accidents can be reduced but cannot be avoided because of risky and uncertain nature of construction works. Construction accidents tend to cause serious damages, injuries, fatalities, and work interruptions, and provoke disputes among the involved parties. One useful means for resolving the dispute through alternative dispute resolution, instead of costly litigation, is to find similar accidents occurring in the past in the same jurisdiction and compare their similarities and differences. This paper introduces a research project on the effective retrieval of relevant historical cases from a case library using text mining techniques. Research issues in the text mining process, especially how to represent the unstructured textual cases by a structured term vector model, are investigated. It is concluded that natural language based case document retrieval is superior to the case-based reasoning from structured case collection and is more practical for implementation in a construction management information system. [Copyright &y& Elsevier]

Construction equipment constitutes a significant portion of investment in fixed assets by large contractors. To make the right decisions on equipment repair, rebuilding, disposal, or equipment fleet optimization to maximize the return of investment, the contractors need to predict the residual value of heavy construction equipment to an acceptable level of accuracy. Current practice of using rule-of-thumb or statistical regression methods cannot satisfactorily capture the dynamic relationship between the residual value of a piece of heavy equipment and its influencing factors, and such rules or models are difficult to integrate into a decision support system. This paper introduces a data mining based approach for estimating the residual value of heavy construction equipment using a predictive data mining model, and its potential benefits on the decision making of construction equipment management. Compared to the current practice of assessing equipment residual values, the proposed approach demonstrates advantages of ease of use, better interpretability, and adequate accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Innovations in construction equipment using cognitive and automation techniques, as well as computerized equipment Information Management System (IMS) have greatly simplified the equipment operations and management process. Though the data collection, storage and reporting for equipment management are no longer pressing issues for the company equipment manager, data analysis becomes increasingly difficult with large amounts of data, especially for identifying potential problems in operations and management of a large construction fleet. The proposed decision support system for equipment management uses a resolution-based outlier definition and a nonparametric outlier mining algorithm that can automatically detect inconsistent observations from a large equipment dataset, and rank the records based on their degree of inconsistency. The nonparametric outlier mining algorithm demonstrates ease of use, high flexibility and satisfactory results in construction equipment management. [Copyright &y& Elsevier]

Aiming at safety regulation in the operation of major infrastructure projects (MIPs) to prevent potential risk loss and adverse social impacts, this research presents a novel model integrating evolutionary game and system dynamics (SD) for optimizing safety regulation strategies with different stakeholders involving the operating company (OC), government section (GS), and public under the bounded rationality, where the evolutionary game theory is applied to describe the interactions among stakeholders in the safety regulation of MIPs followed by simulating through adopting the SD to analyze the effects of different strategies on equilibrium solutions and the stability of game equilibrium. In view of the simulation results based on five scenarios, the dynamic penalty-incentive scenario not only effectively restrains the fluctuations of the strategy selection, but also provides an ideal evolutionary stable strategy, in which the OC could nearly choose to comply with the regulations, while the public could nearly choose to supervise the OC as their optimal strategy to prevent risks. All results indicate that the application of the evolutionary game with the SD model is an effective way to analyze the effects of different strategies and provide effective solutions to study complex multi-player game problems. Overall, this research contributes to developing an evolutionary game with the SD model for the safety regulation of MIPs, which can serve as a platform to identify reasonable regulatory strategies with great practical application. [ABSTRACT FROM AUTHOR]

• DEA and distance friction minimization are used to identify the causes of inefficiency and sources of growth in regional construction industry of China. • Target-oriented approach is applied to set stagewise targets and plan for an incremental improvement of regional construction productive efficiency. • Stepwise growth of construction productive efficiency is more practical due to the complex nature of construction industry. • Recommendations are made for policy formulation and strategic planning in regional construction industry of China. China's construction industry is a major contributor to economic growth during the past decade, but the industry has been mainly investment-driven without significant productivity improvement yet with high disparity across different regions. In the context of unbalanced regional development and resource allocation, stagnant performance of construction productivity is a common concern hindering the rapid and sustainable development of regional construction industry. The long-standing and escalating regional differences would not only undermine the overall performance of construction productivity, also lead to inefficient resource allocation and inappropriate target settings for future development. To address these issues, this study developed an overall efficiency measurement model for the construction industry, and then applied a Target-Oriented (TO) Data Envelopment Analysis (DEA) model to identify the optimal pathways for low efficiency provinces with the achievement of stage-wise targets in different regions of China. Comparison was made on construction productive efficiency, resource utilization, and recommendations were made for further improvement in three regions with different levels of economic development. The analytical and empirical results can provide insightful information for policy making and strategic planning with progressive efficiency improvement of regional construction industry of China. [ABSTRACT FROM AUTHOR]

Aiming to automatically segment multi-class objects on the tunnel point cloud, a deep learning network named dual attention-based point cloud network (DAPCNet) is developed in this paper to act on point clouds for segmentation. In the developed model, data normalization and feature aggregation are first processed to eliminate data discrepancies and enhance local features, after which the processed data are input into the built network layers based on the encoder-decoder architecture coupled with an improved 3D dual attention module to extract and learn features. Furthermore, a custom loss function called Facal Cross-Entropy ("FacalCE") is designed to enhance the model's ability to extract and learn features while addressing imbalanced data distribution. To validate the effectiveness and feasibility of the developed model, a dataset of tunnel point clouds collected from a real engineering project in China is employed. The experimental results indicate that (1) the developed model has excellent performance with Mean Intersection over Union (MIoU) of 0.8597, (2) the improved 3D dual attention module and "FacalCE" contribute to the model performance, respectively, and (3) the developed model is superior to other state-of-the-art methods, such as PointNet and DGCNN. In summary, the DAPCNet model exhibits exceptional performance, offering effective and accurate results for segmenting multi-class objects within tunnel point clouds. • A deep learning method named DAPCNet is developed for 3D point cloud segmentation. • An improved 3D dual attention module is introduced to enhance model performance. • A custom loss "FacalCE" is designed to strengthen feature learning with handling data imbalance. • Conducting comparisons to examine the model performance for segmentation. • The developed method demonstrates outstanding performance, achieving an MIoU score of 0.8597 when applied to tunnel point clouds. [ABSTRACT FROM AUTHOR]

With the increasing complexity of architecture, engineering, and construction (AEC) projects and fast track execution of project works, written documents are becoming more and more important for project coordination, communication, and works control. Finding all the relevant information from unstructured construction documents is critical to various management tasks such as work planning, progress control, and claims. A framework is proposed in this research to retrieve project-wide as-needed information from AEC documents. Through this framework, improvement in the levels of precision and recall in the information retrieval process can be made effective through the use of a project-specific term dictionary and dependency grammar parsing information of textual documents. Their effectiveness is demonstrated through a series of experimental tests conducted on a real life building redevelopment project with different information retrieval and ranking strategies. The results and findings are presented in this paper along with discussion on the related issues on research and system development. [ABSTRACT FROM AUTHOR]

Abstract: The strategic implementation of innovative, collaborative and integrated information is essential for the sustainable development of construction organizations in the new century. The emerging information technologies (ITs) offer construction organizations great potential to develop collaborative work (CW) management information systems in architecture, engineering and construction (A/E/C) projects. This paper presents an in-depth literature review of IT supported CW in A/E/C projects. Based on selected papers from well-known academic journals in construction management over a ten-year period (2000–2009), the review focuses on the implementations of IT in collaborative design, collaborative construction project management, and integrated inter-organization management information systems. A synthesis is presented on the state-of-the-art and trends of IT supported CW in A/E/C projects. Some limitations of past research studies in IT supported CW in A/E/C projects are discussed and future research directions are recommended. [Copyright &y& Elsevier]

Prefabrication housing production (PHP) is widely promoted due to its potential benefits, such as reduced construction time and improved quality. However, PHP also faces many uncertainties and constraints due to the highly fragmented process of managing design, construction, and supply chain. Lean construction principles and information technologies, e.g., radio frequency identification (RFID) and building information modeling (BIM), have proven to be effective in helping to reduce the uncertainties and remove the constraints in PHP. However, the availability of appropriate training or pedagogical approaches to transfer and share the lean knowledge and information techniques has impeded their adoption. To address the issue, this study develops a hands-on learning tool, which is an advanced simulation game called RBL-PHP [i.e., RFID/BIM/lean (RBL)-PHP]. The RBL-PHP simulates the process of PHP from manufacturing and logistics to the on-site assembly by integrating an RFID-enabled BIM platform with lean construction into training students and practitioners. Four workshops are conducted at Hong Kong Polytechnic University, Hong Kong, China, to assess the participants' learning experience by using ex-ante and ex-post surveys. In each workshop, the performance of PHP is tested separately in a traditional round and a RBL-enabled round on indicators including the percentage of plan complete (PPC), extra cost, and the productivity index. The results indicate that the stability and efficiency of PHP are improved in the RBL-enabled round. In addition, the simulation game can significantly improve the understanding of various knowledge concepts in PHP, such as prefabrication production structure, standardized work, smart construction objects, information sharing and communication, and real-time tracking and visualization, when compared with a traditional computer-based multimedia presentation approach. It can be adopted as a useful platform to effectively train practitioners in the PHP sector on the use of lean, BIM, and other innovative information technologies. [ABSTRACT FROM AUTHOR]