ORCID Profile
0000-0001-9976-1253
Current Organisations
Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences
,
The University of Tennessee Knoxville
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Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 02-2022
Publisher: MDPI AG
Date: 17-02-2022
Abstract: The U.S. is the second largest contributor to carbon emissions in the world, with its road transport sector being one of the most significant emission sources. However, few studies have been conducted on factors influencing the emissions changes for the U.S. from the perspective of passenger and freight transport. This study aimed to evaluate the carbon emissions from the U.S. road passenger and freight transport sectors, using a Logarithmic Mean Divisia Index approach. Emissions from 2008 to 2017 in the U.S. road transport sector were analysed and key findings include: (1) energy intensity and passenger transport intensity are critical for reducing emissions from road passenger transport, and transport structure change is causing a shift in emissions between different passenger transport modes and (2) the most effective strategies to reduce carbon emissions in the road freight transport sector are to improve energy intensity and reduce freight transport intensity. Several policy recommendations regarding reducing energy and transport intensity are proposed. The results and policy recommendations are expected to provide useful references for policy makers to form carbon emissions reduction strategies for the road transport sector.
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 08-2021
Publisher: Emerald
Date: 24-03-2022
DOI: 10.1108/ECAM-08-2019-0438
Abstract: The government plays a critical role in driving building information modeling (BIM) implementation. The purpose of this study is to investigate the government efforts for driving BIM implementation in three benchmark countries, namely, Singapore, the UK and the US, so as to develop appropriate roadmaps for increasing BIM implementations in other countries. This study performs a review on the government efforts and roles in BIM implementation in three benchmark countries, namely, Singapore, the UK and the US. Through cross comparison with existing literature, it is found that Singapore and the UK adopt a government-driven approach and a phase-by-phase development pattern is observed. The first phase focuses on the building sector to rapidly increase the use of BIM and the government generally plays the role of an initiator. In the second phase, BIM is expanded to other implementation areas, e.g. smart city. The importance of the initiator role decreases and more attention is paid to supporting roles such as researcher, educator and regulator. In contrast, an industry-driven approach is adopted in the US. The main role of the government is that of a regulator, with research institutions actively supporting the BIM implementation. General roadmaps of the two mandating approaches are presented. The results can provide a useful reference for countries and regions that intend to develop roadmaps to increase their BIM maturity level and enhance readiness to accept and implement BIM. This study is one of the first studies that investigate the step-by-step roadmaps for implementing BIM from the perspective of changing government roles.
Publisher: Wiley
Date: 11-08-2022
DOI: 10.1111/MICE.12904
Abstract: Construction projects face various constraints in terms of materials, labor, equipment, and documents, which can interrupt the scheduled work. Package‐based constraint management (PCM) is a state‐of‐the‐art graph‐based approach that follows the lean theory to effectively model, monitor, and remove constraints before the commencement of work, ensuring smooth construction and minimizing delay and waste. PCM relies on exploring and investigating project knowledge bases (KBs), formed by entity‐relation‐entity triples of constraints. However, most PCM KBs are incomplete and suffer from poor semantics, which hinders the PCM functions. Although many KB completion (KBC) methods exist in the field of artificial intelligence, they primarily focus on general knowledge and exclude the features of specific domains. Therefore, they cannot be directly applied to complete PCM KBs. To address the issue, this study proposes a novel deep learning model, referred to as the domain information enhanced graph neural network (D‐GNN). The features of the developed D‐GNN include (1) building a domain ontology to enrich semantics with rule reasoning, (2) applying the GNN to learn and encode embeddings of constraint entities and relations, and (3) employing a convolution neural network (CNN) for decoding and identifying missing triples. D‐GNN improves the existing KBC methods by integrating two types of domain information, namely, the ontological classes and working contexts into GNN and CNN, respectively. The experimental results verified that the D‐GNN reached an accuracy of 0.848–0.951, and the domain information integration increased the performance by up to 0.263. In practical testing, the D‐GNN significantly reduced the KBC time to 1/6–1/35 of the manual approach and reached higher accuracy. Therefore, the proposed D‐GNN can facilitate PCM by providing complete KBs and supporting downstream constraint monitoring and removal.
Publisher: Emerald
Date: 04-06-2019
DOI: 10.1108/ECAM-07-2018-0281
Abstract: Employing multi-type laborers (MLs) is common in multinational and cross-culture projects (MPCs). Different attributes of MLs can lead to uncertain and dynamic laborer behaviors (i.e. behavioral ersities), which may cause project deviations. Previous studies do not consider the uncertainties or dynamics of behaviors adequately or they only provide general suggestions. The purpose of this paper is to combine system dynamics (SD) and agent-based modeling (ABM) to build an integrated model. The proposed ABM-SD can gain better understanding of MLs’ behavioral ersities, reveal the associated impacts and improve project management. Based on extensively review in construction labor management and computer simulation, architecture is built to depict the relationships between the affecting factors of MLs’ behaviors, MLs’ behavioral ersities and project performance. Second, conceptual structures of the ABM-SD model are developed. Third, methods to implement the model in practice are introduced, focusing on data collection and model structure adjustment. Finally, the model is tested in a case study. Different ML groups have distinctive behaviors which constantly change through interactions between MLs, engineers and external environment. Inadequate consideration of the ersities can result in inaccurate estimation of productivity, work quality and absenteeism, causing severe project deviations such as schedule delay, cost overrun and high absenteeism. On the other hand, using the ABM-SD model, the root causes of project deviations are analyzed from the perspective of MLs’ behavioral ersities and the optimization of labor management can significantly improve project performance. This paper supplements previous studies because the ABM-SD model takes fully use of the strength of simulation of solving uncertain and dynamic problems and combines both qualitative and quantitative findings in existing studies of labor management. Besides, the ABM-SD model is also a practical management tool to better monitor laborer behaviors and forecast the impacts. The limitation is mainly about the small scale of the case study. However, the ABM-SD model already demonstrates the mechanism about how MLs’ different behaviors affect a project, which fulfill the aim of the study. The ABM-SD model can simulate MLs’ behavioral ersities and produce reliable estimations of project performance. It also allows to optimize management plans. Furthermore, The ABM-SD model is adjustable based on specific project conditions, which make it applicable for different tasks, different laborer compositions and even different projects. Thus, the ABM-SD model can be a practical tool for engineers in MCPs. SD and ABM are applied to study behaviors with well-known benefits in both separated and integrated manner. However, few studies use the approach to investigate MLs’ behaviors in MCPs. Hence, the proposed ABM-SD model is an original attempt to improve the laborer management level in MCPs.
Publisher: Elsevier BV
Date: 2016
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 02-2018
Publisher: Elsevier BV
Date: 11-2022
Publisher: Informa UK Limited
Date: 09-11-2022
Publisher: Elsevier BV
Date: 07-2021
Publisher: Elsevier BV
Date: 02-2023
Publisher: Emerald
Date: 11-12-2022
DOI: 10.1108/ECAM-07-2019-0399
Abstract: Multiutility tunnel (MUT) has been recognised as a more sustainable method to place underground utilities than the traditional directly buried (DB) method. However, the implementation of MUT is hindered because of high initial construction costs and the difficulty to demonstrate its benefits, especially social benefits that are hard to be quantified. To address the limitation, this paper aims to quantify and compare both economic costs and traveller loss (i.e. an important part of social costs) of the MUT and DB method. An agent-based model (ABM) is developed, which considers attributes and actions of vehicles, interactions between vehicles and interactions between vehicles and the road network. The ABM is used to estimate traveller loss by comparing traveller time when the MUT and DB method is adopted, respectively. The traveller loss is combined with economic costs to estimate and compare the LCC of the MUT and DB method. To verify the ABM-based approach, it is implemented in an MUT project in Shanghai, China. Results of the study indicate: (1) When the DB method is adopted, periodic E& Rs cause severe traffic congestion and substantial traveller loss. (2) When traveller loss is not included in the LCC estimation, the DB method has a lower LCC in most scenarios. (3) When traveller loss is included, the relative LCC of MUT and the time it takes to cover the LCC of the MUT and DB method is largely reduced. Thus, when social costs are considered, MUT will bring more benefits than the DB method. Previous studies on comparing the MUT and DB method focus on investigating economic costs, while other costs, e.g. social costs, are not well addressed quantitatively. Besides, current studies of traveller loss estimation lack consideration of factors such as unique attributes, actions and interactions of vehicles and the network. Hence, this paper applies an ABM-based approach to involve these factors and produce more reliable estimation of traveller loss than existing approaches. Moreover, by integrating traveller loss into LCC analysis, this paper helps to understand the benefits of MUT thus assisting decision-making in selecting utilities placement methods.
Publisher: Elsevier BV
Date: 02-2022
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 04-2021
Publisher: Springer Science and Business Media LLC
Date: 18-08-2022
DOI: 10.1007/S43503-022-00001-Z
Abstract: The past decade has witnessed a notable transformation in the Architecture, Engineering and Construction (AEC) industry, with efforts made both in the academia and industry to facilitate improvement of efficiency, safety and sustainability in civil projects. Such advances have greatly contributed to a higher level of automation in the lifecycle management of civil assets within a digitalised environment. To integrate all the achievements delivered so far and further step up their progress, this study proposes a novel theory, Engineering Brain, by effectively adopting the Metaverse concept in the field of civil engineering. Specifically, the evolution of the Metaverse and its key supporting technologies are first reviewed then, the Engineering Brain theory is presented, including its theoretical background, key components and their inter-connections. Outlooks of this theory’s implementation within the AEC sector are offered, as a description of the Metaverse of future engineering. Through a comparison between the proposed Engineering Brain theory and the Metaverse, their relationships are illustrated and how Engineering Brain may function as the Metaverse for future engineering is further explored. Providing an innovative insight into the future engineering sector, this study can potentially guide the entire industry towards its new era based on the Metaverse environment.
Location: United Kingdom of Great Britain and Northern Ireland
Location: China
Location: United States of America
Location: No location found
No related grants have been discovered for CHENGKE WU.