Construction Projects In China Research Articles

Research on BIM sub-model data of construction engineering based on MVD

BIM technology has been widely applied to various stages of construction projects in China. The data required at each stage and scenario of construction projects varies, necessitating the definition of target BIM data according to data needs. When BIM software generates IFC models, different MVD templates can be configured to output BIM sub-models. To study the characteristics of data configuration for different MVDs, this article takes construction projects as an example and designs an analysis experiment for BIM sub-model output based on MVD. It analyzes the differences and characteristics of IFC model data from both the overall project level and the professional component level according to key indicators, with the aim of providing references for construction project personnel to select matching MVDs, ensuring the effectiveness of BIM sub-models, and meeting the needs of business scenario applications.

Experimental Course Reform of Applied Chemistry Specialty Under the Background of "Double First-class" Construction

In recent years, with the continuous promotion of the "double first-class" construction project in China, it has pointed out the direction for the future development of most universities in China. In this process, colleges and universities can optimize and improve the content, method and system of courses according to the development objectives of the specialty by increasing the proportion of practical courses, combining the characteristics and advantages of the specialty of applied chemistry, so as to cultivate a group of high-quality, high-level and high-capacity applied chemistry professionals for the society.

Exploration of the Application of New Building Materials and Technologies in Residential Engineering

With the development of the economy and social progress, the number of residential construction projects in China is constantly increasing, and building materials are also constantly developing and improving. The application of new materials and technologies in residential engineering in China mainly aims to improve the quality of residential construction, reduce environmental pollution, and meet people's requirements for residential buildings. In the new situation, the country has put forward higher requirements for residential construction, so it is necessary to pay attention to the application of building materials and technology. This article explores the application of new building materials and technologies in residential engineering, aiming to provide reference for relevant personnel.

Dynamic risk early warning system for tunnel construction based on two-dimensional cloud model

Due to the uncertainty of geological conditions and construction status during tunnel construction, the risk of tunnel construction will vary with updates to environmental information. The current risk management practice for tunnel construction projects in China is static and relies on the subjective judgment of experts and practitioners, so it is necessary to propose a risk assessment methodology that takes into account dynamically updated risk information and uncertainty. This paper proposes a dynamic risk warning system for tunnel construction that integrates knowledge-driven consequence datasets and data-driven likelihood datasets of risk events, using a probability algorithm based on a two-dimensional cloud model for risk decision-making. The algorithm quantifies the risk probabilities by randomly simulating the risk discrimination results under the influence of uncertainty multiple times and statistically analyzing the distribution pattern of a large number of simulation results within the constructed quantitative risk matrix. Decisions on risk level and warning state are made based on risk probabilities. Moreover, this study conducts extensive simulation tests to determine the optimal number of simulated cloud droplets and develops a software toolbox to facilitate rapid feedback on tunnel construction risk. The proposed algorithm is applied to the construction of Guiyang Metro Line 3. The results show that the standard deviation of the simulation results is less than 0.025 and the algorithm has good stability. The two-dimensional cloud model offers a superior reflection of risk uncertainty compared with its one-dimensional counterpart, making it more appropriate for assessing the risks associated with tunnel construction—a complex scenario characterized by intricate index relationships and significant uncertainty.

Circular design strategies and economic sustainability of construction projects in china: the mediating role of organizational culture

This research aims to elucidate the relationship between circular design strategies (CDS) and the economic sustainability of construction projects (ESCP), examining the mediating role of organizational culture (OC). Motivated by the imperative to develop a sustainable circular economy (CE) model in the building industry, our study focuses on a crucial dimension of CE processes. Specifically, we investigate how construction firms’ organizational values shape their pursuit of desired economic outcomes within CE theory. Through a comprehensive analysis of 359 responses from a cross-sectional survey of Chinese construction firms employing Partial Least Squares-Structural Equation Modeling (PLS-SEM), our findings reveal a positive albeit weakly impactful association between CDS and ESCP. Simultaneously, OC is identified as a factor detrimental to ESCP. Notably, this study unveils the influential roles of hierarchical culture (HC) and group culture (GC) in shaping the current state of ESCP in China. Emphasizing the significance of CDS, we propose that contract administrators proactively reposition their organizations to adopt strategies conducive to achieving the necessary economic output for construction projects. The originality aspect lies in this research contributes to the existing body of knowledge by offering empirical insights into the theoretical framework, marking the first such empirical study in northern China. We conclude by critically examining research outcomes and limitations while providing insightful recommendations for future research to foster sustainable construction practices in the Chinese context.

Application of the Sustainable Sites Initiative Rating System in Urban Green Space Construction in China—The Case of Xuhui Runway Park

The Sustainable Sites Initiative (SITES) is a comprehensive evaluation system for sustainable landscape, which has been promoted successfully and widely recognized in the landscape architecture industry. However, the existing research lacks attention to the applicability of SITES to urban green space construction projects in China. Therefore, exploring the application of the SITES evaluation system in the context of Chinese urban green space development is of great importance. Building upon theoretical research on sustainable landscapes, this study focuses on urban green space projects as the research carrier, with SITES as the subject of investigation. Utilizing the case study method, a thorough description and analysis of Xuhui Runway Park, a representative case officially certified by SITES in China, is provided. Across four stages—project initiation and planning, scheme design, construction, and operation—this study discusses the approaches employed in Chinese urban green space projects to meet the requirements of the SITES evaluation system. By evaluating the applicability of SITES to Chinese urban green space projects, identifying limitations in system promotion, and offering references for sustainable design practices, this study advances the application and promotion of SITES in China and contributes to the sustainable development of the landscape architecture industry.

Research on Differences in Knowledge Demand of Sustainability Learning of Teams in Mega Engineering Construction Project in China

Knowledge plays a vital role in achieving sustainable development goals (SDGs) in construction industry. According to the view of organization learning, heterogeneous teams have heterogeneous core knowledge. The main aim of this study is to explore the differences in demand for sustainability learning knowledge (SLK) among the mega engineering construction project (MECP) teams, which has important implications for improving the performance of team learning and achieving SDGs in MECPs. This study followed a research design that included identifying SLK and the stakeholder teams that needed SLK and investigating the respondents’ perception of the level of demands for SLK through semistructured interviews and survey questionnaires executed in China. The statistical test results indicated that there were some significant differences in demands for some SLK among the MECP teams and MECPs. All types of MECP teams had a common high demand for SLK on environment, safety, and health in China. This study contributes to research on sustainable construction in MECPs in two main ways: (1) it reveals novel insights related to the integration of SL into the MECPs sustainable construction practices for developing countries and (2) it offers the possibility for practitioners to identify the most crucial knowledge and focus their efforts and resources efficiently.

Data-driven real-time prediction for attitude and position of super-large diameter shield using a hybrid deep learning approach

The presented research introduces a novel hybrid deep learning approach for the dynamic prediction of the attitude and position of super-large diameter shields - a critical consideration for construction safety and tunnel lining quality. This study proposes a hybrid deep learning approach for predicting dynamic attitude and position prediction of super-large diameter shield. The approach consists of principal component analysis (PCA) and temporal convolutional network (TCN). The former is used for employing feature level fusion based on features of the shield data to reduce uncertainty, improve accuracy and the data effect, and 9 sets of required principal component characteristic data are obtained. The latter is adopted to process sequence data in predicting the dynamic attitude and position for the advantages and potential of convolution network. The approach’s effectiveness is exemplified using data from a tunnel construction project in China. The obtained results show remarkable accuracy in predicting the global attitude and position, with an average error ratio of less than 2 mm on four shield outputs in 97.30% of cases. Moreover, the approach displays strong performance in accurately predicting sudden fluctuations in shield attitude and position, with an average prediction accuracy of 89.68%. The proposed hybrid model demonstrates superiority over TCN, long short-term memory (LSTM), and recurrent neural network (RNN) in multiple indexes. Shapley additive exPlanations (SHAP) analysis is also performed to investigate the significance of different data features in the prediction process. This study provides a real-time warning for the shield driver to adjust the attitude and position of super-large diameter shields.

Evaluation of Key Factors for Promoting Green Construction Practices Based on a Hybrid Decision-Making Framework: A Case Study on the Renovation of Old Residential Communities in China

The multi-criteria group decision-making approach, rooted in fuzzy mathematics theory, is employed to address the globally significant issue of green construction in this paper. The construction industry is one of the most prominent contributors to carbon emissions, which is evident throughout the entire supply chain. Meanwhile, the renovation of old residential communities is a large-scale urban construction project in China. In striving to advance the sustainable development of the construction industry and meet decarbonization goals, the attention has shifted towards green construction in the renovation of old residential communities. However, substantial gaps persist in both technical innovation and practical application of green construction. This study aims to identify the factors that influence the popularization and implementation of green construction practices in the renovation of old residential communities. To achieve this, a comprehensive decision-making approach is sought, and these factors are thoroughly investigated. In this paper, we propose a hybrid decision-making framework that incorporates grounded theory, the fuzzy decision-making and trial evaluation laboratory (DEMATEL) method, and the analytic network process (ANP) method. Leveraging triangular fuzzy numbers and other fuzzy mathematical theories, this approach is designed to assess the factors that influence the popularization and implementation of green construction practices, uncovering their interrelationships and mechanisms. The results indicate the comprehensive nature of advancing green construction practices, encompassing the entire supply chain involved in the renovation of old residential communities. Among these factors, social participation and market environment emerge as the most influential. Building upon these conclusions, this paper offers specific recommendations. Ultimately, this study equips the construction industry with both a theoretical foundation and a methodological framework to popularize and implement green construction practices effectively.

The integration of BIM in the application of green building materials in construction projects

In recent years, with the growing call for sustainable development, green building materials, which are of great meaning for reducing resource and environmental impacts, have attracted more and more attention. However, in China, a country with frequent construction activities and high resource consumption, the application of green building materials in construction projects still faces challenges. Building Information Modelling (BIM) as an emerging technology in the construction industry can increase the adoption rate of green building materials in construction projects. The aim of this study is to identify the barriers encountered in the application of green building materials in Chinese construction projects, and to propose corresponding improvement measures by integrating BIM in the decision making. This study adapted a qualitative research method, using semi-structured interviews as a research tool. A total of 6 respondents (including project managers, architects, engineers, and researchers) from Chinese construction companies and research institutions collected interview data, and used Nvivo software to analyze and summarize. The results of this study show that although the application of green building materials in construction projects in China still faces challenges, including market acceptance and supply chain issues, cost and economic feasibility issues, and lack of relevant technology and knowledge. In addition, the study found that BIM technology made a great contribution in promoting the application of green building materials. Through BIM technology, the whole life cycle information management such as material selection, engineering simulation and performance evaluation of construction projects can be carried out, so as to improve the application of green building materials. By exploring the obstacles to the application of green building materials in China’s construction projects and combining with the promotion of BIM technology, this study puts forward a series of specific suggestions and solutions to foster the application of green building materials. This research has made certain theoretical and practical contributions to foster the application of green building materials for the sustainable development of China's construction industry.

The Nexus Between Construction Contract Risk Assessment and Big Data in China

The construction market in China is currently showing an increasing trend in terms of project size, complexity, and technical difficulty. With large initial investments and long capital payback periods, construction projects are vulnerable to the external environment during development and risk control is particularly important. Contracts are at the heart of project management and every step of project development revolves around the content of the contract signed by the contractor. Signing contracts to protect the rights of both parties has become the most effective way of controlling project risk. And with big data technology being rapidly developed and widely used, and the amount of data created and accumulated by the internet growing exponentially, big data has the potential to be used for analysis, forecasting and decision making in the construction industry. The aim of this study is therefore to develop a risk identification list for construction project contracts in China, to score the risks within the list according to their incidence and importance, and to find practical applications of big data technology in contract risk assessment. To achieve this goal, this study used qualitative research and in-depth interviews with experts within the Chinese construction engineering industry. The results of this study form a list of risks in Chinese construction contracts, indicating that late payment and government intervention are the most important risks in Chinese construction contracts. This study also identifies TianYanCha, a commonly used big data enterprise risk identification platform in China, which provides big data support in three areas: evaluating owner integrity, enterprise risk identification, and owner relationship knowledge. At the practical level, this study helps contractors to assess project risks and improve contractual contents during the preparation phase of construction projects, thus improving the success rate of projects. At the theoretical level, this study fills the gap in contractual risk assessment for construction projects in China and provides insights for subsequent risk assessment of construction projects based on the contractor's perspective.

Risk Control Analysis in Electric Power Engineering Project Management

With the development of social economy, the number and scale of electric power construction projects are increasing. Their contents are changing from "single" in the past to "diversified" at present. And the contents of project management are constantly being improved and updated. The function and structure of the electric power construction project in China are relatively complex, involving many factors. There are certain risks in safety, quality, investment, construction period and operating benefit. How to better solve the problems existing in the actual operation process and how to better manage power companies and improve the efficiency and economy of power company projects are the key issues that need to be considered. In order to ensure the economic and social benefits of electric power projects, improve their engineering quality, the project managers must analyze various possible risk factors in time during the construction of electric power projects, and take effective measures to minimize the risks.

Construction pit deformation measurement technology based on neural network algorithm

Abstract The current technology of foundation pit deformation measurement is inefficient, and its accuracy is not ideal. Therefore, an intelligent prediction model of foundation pit deformation based on back propagation neural network (BPNN) is proposed to predict the foundation pit deformation intelligently, with high accuracy and efficiency, so as to improve the safety of the project. Firstly, to address the shortcomings of BPNNs, which rely on the initial parameter settings and tend to fall into local optimum and unstable performance, this study adopts the modified particle swarm optimization (MPSO) to optimise the parameters of BPNNs and constructs a pit deformation prediction model based on the MPSO–BP algorithm to achieve predictive measurements of pit deformation. After training and testing the data samples, the results show that the prediction accuracy of the MPSO–BP pit deformation prediction model is 99.76%, which is 2.25% higher than that of the particle swarm optimization–back propagation (PSO–BP) pit deformation prediction model and 3.01% higher than that of the BP pit deformation prediction model. The aforementioned results show that the MPSO–BP pit deformation prediction model proposed in this study can effectively predict the pit deformation variables of construction projects and provide data support for the protective measures of the staff, which is helpful for the cause of construction projects in China.

Drivers of Engineering Procurement and Construction Model Adoption Behavior by Public Construction Owners in China

The promotion of the EPC model in public construction projects is a priority for reform in the Chinese construction industry. This study integrates the push–pull–mooring (PPM) model with the stimulus-organism-response (SOR) framework to explore the influencing factors and action mechanisms of adopting the EPC model of the owners of public construction projects in China. An online questionnaire was sent to public construction project owners who have participated in EPC projects. Partial least squares-structural equation modeling (PLS-SEM) was employed to analyze the survey data. The results identified that the push factors for public construction project owners to the adoption of the EPC model include owners’ management ability, owner’s knowledge about EPC, and the matching degree between the owner’s management philosophy and EPC model; the pull factor is the performance pressure of the project; and the mooring factors include subjective norms, the maturity degree of the EPC, and the support environment. In addition, perceived usefulness, cognitive, and adoption attitude play a mediating role in the relationship between external stimuli and adoption behavior. Overall, this study enhances the understanding of Chinese public project owners’ behavior in adopting the EPC model and provides a theoretical basis for policy recommendations to promote the adoption of the EPC model by public construction project owners in China.

Earthwork digital twin for teleoperation of an automated bulldozer in edge dumping

AbstractThe unstructured construction site with a complex and changeable environment brings challenges to human–machine interaction. In this study, the earthwork digital twin (DT) is proposed to realize accurate and real‐time perception, which can support the teleoperation of an automated bulldozer. The overall framework includes anterior‐time DT, real‐time DT, and posterior‐time DT, which integrates three‐dimensional modeling, edge detection, and blade trajectory algorithms. Multidimensional heterogeneous data in bulldozer teleoperation can be displayed in real‐time to ensure construction safety. Big data during teleoperation of the construction process can be collected, stored, and analyzed. The DT proposed in this study was successfully applied in a major construction project in China, and testing results show its universality, robustness, and advanced performance. The key technologies proposed in this study can be applied to solve the common problems in the construction industry, which is promising for future intelligent construction.

A study on the key drivers of national construction project audit risk and evolvement mechanism

PurposeChina has recently introduced a new audit law that aims to increase the scope of audit supervision and raise the standards for preventing risks in auditing national public projects. This paper presents a systematic research study on the causes of audit risks in national public projects and discusses the process by which these causes contribute to the emergence of such risks. Furthermore, the paper investigates the core risk sources in various types of national construction project audit. This paper aims to provide theoretical support for auditors of national construction projects in risk avoidance when conducting audits.Design/methodology/approachIn this study, the authors carefully selected five national public audit projects from China and performed a comprehensive analysis of 85 relevant audit documentation. The textual analysis was conducted using Nvivo12 software, and the grounded theory approach was adopted for generalization purposes.FindingsBased on the research results, the findings suggest that there are five key causes contributing to the audit risk of national construction projects: professional competence, risk awareness, management capacity, level of attention and deliberate fraud. The most critical factor identified is management capability, with 59.93% of the data supporting this view. This conclusion was based on an analysis of state-owned enterprises, administrative organs and public institutions. Building upon this, a framework titled “the mechanism of audit risk factors with management capability as the core” was constructed.Originality/valueThis paper employs qualitative analysis methods to examine national construction projects in China, contributing new literature to the theoretical study of audit risk management. The article also provides practical recommendations for auditors on how to mitigate audit risks and improve the quality of audit services in national project governance.

System based greenhouse emission analysis of off-site prefabrication: a comparative study of residential projects

High-story residential structures and off-site prefabrication have been dominant choices in the construction industry. There is a substantial quantity of greenhouse gas (GHG) emissions produced by the construction industry. In fact, the construction industry is responsible for 30 percent of all GHG emissions. In this study, we analyse the differences between the conventional technique of building and the off-site prefabricating construction method. First, we evaluate the emissions emitted from key processes during the off-site prefabricating construction. In addition, we analyse the qualitative and quantitative differences between two prefabrication structural systems, namely concrete and steel, which are the two most common structural systems utilised in residential construction projects in China. We examine and analyse four different case studies in order to exemplify the proposed methodology and offer managerial insights.

How Team Voice Contributes to Construction Project Performance: The Mediating Role of Project Learning and Project Reflexivity

Voice behavior has been widely recognized as having various benefits for organizations, while researchers seldom notice its consequences in construction projects. Against this context, this study empirically explores the impact path of team voice on project performance in construction projects from a multi-team system (MTS) perspective. Considering the essence of voice behavior is to provide information, this study introduces the input–process–outcome (IPO) framework to verify how team voice can affect project performance by influencing project learning and project reflexivity. The online survey method, monetary incentive method, and chain-referral sampling method are adopted to distribute the designed questionnaire. A total of 184 data points from completed construction projects in China supported the research conclusion by adopting the partial least squares structural equation modeling (PLS-SEM) technique. Project learning and project reflexivity positively mediate the relationship between team voice and project performance in construction projects. Specifically, team voice can first positively influence project learning and project reflexivity, which both have a positive relationship with project performance. For theory, this study opens the black box between team voice and project performance in the context of construction projects by unveiling the mediating path of project learning and project reflexivity, which also enriches the literature on voice behavior and expands its application. Furthermore, this study provides one new perspective for MTS knowledge by adopting the IPO framework to explore the underlying mechanism between the focal team and the overall team performance of the MTS. For practice, this study has a directive function for construction project management and MTS management, while providing reminders for policymakers, researchers, and practitioners about the significance of project learning and project reflexivity on project performance.

Cost Benefit Analysis of Public Construction Projects under PPP Operation Mode

<p>The public-private partnership (PPP) model that the state vigorously promotes is becoming a focus of stabilizing growth and adjusting structure. However, the application of the PPP model in public construction projects in China is still in its infancy. When the government makes project decisions, how to clarify the project risks, requirements and opportunities undertaken by all participants and weigh the external benefits brought by the PPP model has become an urgent problem to be solved to promote the reform of public service projects in China. Starting from practice, this paper reviews the relationship between school infrastructure investment and student performance involved in new school projects and the pros and cons of introducing the PPP model in the education field under the PPP framework. By comparing new PPP schools and non-PPP new schools in Victoria, Australia, the case is analyzed in multiple dimensions, it is concluded that there are indeed differences in operating expenditure and education performance between PPP schools and non –PPP schools. PPP schools have higher operating expenses than non-PPP schools and their educational achievements are also more prominent. The cumulative curve shows that the PoF of PPP school project operating expenditure is 48.37%, slightly higher than 48.05% of non-PPP school project. According to conclusion of this case data statistics, the introduction of the PPP model is explained to increase school infrastructure investment and promote better educational outcomes to a certain extent. School projects under the PPP framework can promote good educational achievements and provide theoretical support and countermeasures.</p>

How and when creativity thrives in construction projects: a study of leader's humor

PurposeThe present study aims to advance a new framework to spur creativity at individual and team levels in the construction industry by studying a leader's sense of humor. The authors develop and test a multi-level model to investigate the direct and indirect effects of leader's humor on creativity within teams working on construction projects. The authors draw on the benign violation theory to hypothesize that a leader's sense of humor influences the acceptability of norm violations in teams, which helps to improve their creativity. The authors also integrate the benign violation theory with the social information processing theory to examine the indirect effects of project leader's sense of humor on individual- and team-level creativity via team psychological safety.Design/methodology/approachThe authors collected data from 165 members nested in 45 teams working on construction projects in China. The construct's factor structure and discriminant validity were established through confirmatory factor analysis. The authors used multi-level modeling via Mplus to test team-level to the individual-level direct and indirect hypotheses, while team-level direct and indirect hypotheses were tested using ordinary least squares regression.FindingsThe results show that the leader's humor has a dual positive direct effect on individual and team creativity. Furthermore, these effects are partially mediated by team psychological safety. The implications of these findings to improve the construction management theory and practice are discussed in the manuscript.Originality/valueThe current study contributes to the literature by understanding the significance of leader humor in predicting individual-level and team-level creativity within the construction sector. It expands the literature by examining team psychological safety as the underlying mechanism in the relationship between leader humor and creativity.