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Building information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase.

An SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation.

The simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM.

This study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.

This paper aims to propose the practice of urban resilience to flooding in a vulnerable Asian city, Taipei. It conducts Geographic Information System (GIS)-based simulations to assess Taipei’s vulnerability under a set of varying flooding scenarios and likelihoods. This research concludes by proposing remedies to fill the gaps these flood simulations reveal and, in doing so, promotes urban resilience in Taipei. This paper provides an example of urban resilience to flooding for other cities in Asia.

The paper undertakes a case study of Taipei to survey current practices and historic analyses as a tool to evaluate a chronology of policies implemented to make Taipei resilient to flooding. It also conducts flood simulation and scenario analysis through the technology of GIS by using ArcMap 10.2.2 software to gauge the vulnerabilities in Taipei. These GIS-based data sets are collected from Taiwan’s central and local governments. This paper analyzes the vulnerability of population, land value, residential properties, GDP and critical facilities, such as major subway stations, medical centers, public schools, major public buildings, electric power substations and gas/oil stations. Additionally, it analyzes the likelihood and cost and benefit of different flooding scenarios based on typhoon and rainfall data sets in 1975-2014 period. After a thorough analysis of vulnerability, likelihood of flooding and cost–benefit analyses, this research develops Taipei resilience policies to address the vulnerabilities.

The findings indicate that Taipei case study, a chronology of policies implemented to prevent flooding, explains that costly engineering structures, rebuilding and fortification against floods eventually created a false sense of security, which has encouraged more intensive residential and commercial developments in flood-prone areas, and led to a higher level of vulnerability. Additionally, flooding simulations indicate that 40 per cent of Taipei City is located in flood risk areas in an extreme weather scenario. This percentage is higher than other global cities such as London’s 15 per cent, Tokyo’s 10 per cent and New York City’s 25 per cent. Based on the 10 per cent of total flooding areas above 0.5 m, the vulnerable population is estimated at 200,000 people, or 7 per cent of the total population. The GDP impact will be more than $28bn. More than $67bn of land value is vulnerable. A least one million subway passengers will be affected each day. Further, there is little evidence that the urban poor are particularly vulnerable to floods. On the contrary, some neighborhoods with high-income households face a higher risk of floods. Very few medical centers, oil and gas stations and electrical power substations are located in flood-prone areas, but a large number of public schools, administrative buildings and major subway stations are susceptible. Additionally, the likelihood analysis of flooding in an extreme rainfall scenario concludes that the possibility will be five times that of the existing assumption with a flood in every 200 years. Thus, Taipei City’s infrequent once-in-two-century floods are likely to occur more frequently.

This paper provides a thorough analysis of vulnerability, likelihood of flooding and cost–benefit analyses in Taipei. It also develops Taipei resilience policies to address the vulnerabilities. In the future, rather than strengthening and rebuilding costly structures, Taipei should focus on land-use and environmental planning for resilience. Urban policies should include environmentally responsible development in the face of continued population and economic growth, and being resilient regarding natural disasters. Most important is the need for a strong political commitment and leadership to initiate and implement urban policies toward resilience.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

The purpose of this paper is to investigate different combinations of collaboration strategies to deal with different types of supply chain disruptions, find the best combination, and provide targeting suggestions for investments.

A system dynamics simulation is applied to study a supply chain with three tiers: a producer, a logistics service provider (LSP), and a retailer. There are three types of disruptions to simulate: a producer capacity disruption, an LSP capacity disruption, and a demand disruption. As each tier has the option to choose whether or not to collaborate with the other two tiers, eight (2×2×2) scenarios are generated to represent different combinations of collaboration strategies.

For a producer capacity disruption, both the producer and the LSP should collaborate by providing their surge capacities, while the retailer does not have to collaborate. For an LSP capacity disruption, the producer should not provide its surge capacity, while the LSP should do so; the retailer does not have to collaborate. For a demand disruption, both the producer and the LSP should not provide their surge capacities, while the retailer should not collaborate but play shortage gaming. Targeting suggestions for investments are provided.

Through system dynamics modeling, this study allows the discussion of surge capacity to help supply chain partners and the discussion of shortage gaming when products are oversupplied, in a disruption recovery system over time.

The aim of this paper is to make an attempt to find good values of onsite–offshore team strength; number of hours of communication between business users and onsite team and between onsite and offshore team to reduce cost and improve schedule for re-engineering projects in global software development environment.

The system dynamics technique is used for simulation model construction and policy run experimentation. The experts from Indian software outsourcing industry were consulted for model construction, validation and analysis of policy run results in both co-located and distributed software development environment.

The study results show that there is a drop in the overall team productivity in outsourcing environment by considering the offshore options. But the project cost can be reduced by employing the offshore team for coding and testing work only with minimal training for imparting business knowledge. The research results show that there is a potential to save project cost by being flexible in project schedule.

The study found that there could be substantial cost saving for re-engineering projects with a loss of project schedule when an appropriate onsite–offshore combination is used. The quality and productivity drop, however, were rather small for such combinations. The cost savings are high when re-engineering work is sent to offshore location entirely after completion of requirement analysis work at onsite location and providing training to offshore team in business knowledge The research findings show that there is potential to make large cost savings by being flexible in project schedule for re-engineering projects.

The software project manager can use the model results to divide the software team between onsite and offshore location during various phases of software development in distributed environment.

The study is novel as there is little attempt at finding the team distribution between onsite and offshore location in global software development environment.

With the proliferation of e-commerce companies, express delivery companies must increasingly maintain the efficient expansion of their networks in accordance with growing demands and lower margins in a highly uncertain environment. This paper provides a framework for leveraging demand data to determine sustainable network expansion to fulfill the increasing needs of startups in the express delivery industry.

While the literature points out several hub assignment methods, the authors propose an alternative spherical-clustering algorithm for densely urbanized population environments to strengthen the accuracy and robustness of current models. The authors complement this approach with straightforward mathematical optimization and simulation models to generate and test designs that effectively align environmentally sustainable solutions.

To examine the effects of different degrees of demand variability, the authors analyzed this approach's performance by solving a real-world case study from an express delivery company's primary market. The authors structured a four-stage implementation framework to facilitate practitioners applying the proposed model.

Previous investigations explored driving distances on a spherical surface for facility location. The work considers densely urbanized population and traffic data to simultaneously capture demand patterns and other road dynamics. The inclusion of different population densities and sustainability data in current models is lacking; this paper bridges this gap by posing a novel framework that increases the accuracy of spherical-clustering methods.

China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market.

To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends.

Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios.

On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

This paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic architecture have been illustrated with a concrete real-world case study and decision rules of using the strategic digital twin management decision system (SDMDS) as a more visualized, adaptive and effective model for decision-making.

This paper integrates the concepts of mental and computer models and examines a real case's business operations by applying system dynamics modelling and digital technologies. The enterprise digital twin system with displaying real-world data and simulations for future scenarios demonstrates an improved process of strategic decision-making in the digital age.

The findings reveal that data analytics and the visualized enterprise digital twin system offer better practices for strategic management decisions in the dynamic and constantly changing business world by providing a constant and frequent adjustment on every decision that affects how the business performs over both operational and strategic timescales.

In the digital age and dynamic business environment, the proposed strategic architecture and managerial digital twin system converts the existing conceptual models into an advanced operational model. It can facilitate the development of knowledge visualization and become a more adaptive and effective model for supporting real-time management decision-making by dealing with the complicated dependence of constant flow of data input, output and the feedback loop across business units and boundaries.

This study reveals the green building development path and analyzes the optimal government subsidy equilibrium through evolutionary game theory and numerical simulation. This was done to explore the feasible measures and optimal incentives to achieve higher levels of green building in China.

First, the practice of green building in China was analyzed, and the specific influencing factors and incentive measures for green building development were extracted. Second, China-specific evolutionary game models were constructed between developers and homebuyers under the market regulation and government incentive mechanism scenarios, and the evolutionary paths were analyzed. Finally, real-case numerical simulations were conducted, subsidy impacts were mainly analyzed and optimal subsidy equilibriums were solved.

(1) Simultaneously subsidizing developers and homebuyers proved to be the most effective measure to promote the sustainability of green buildings. (2) The sensitivity of developers and homebuyers to subsidies varied across scenarios, and the optimal subsidy level diminished marginally as building greenness and public awareness increased. (3) The optimal subsidy level for developers was intricately tied to the building greenness benchmark. A higher benchmark intensified the developer’s responsiveness to losses, at which point increasing subsidies were justified. Conversely, a reduction in subsidy might have been appropriate when the benchmark was set at a lower level.

The expeditious advancement of green buildings holds paramount importance for the high-quality development of the construction industry. Nevertheless, the pace of green building expansion in China has experienced a recent deceleration. Drawing insights from the practices of green building in China, the exploration of viable strategies and the determination of optimal government subsidies stand as imperative initiatives. These endeavors aim to propel the acceleration of green building proliferation and materialize high-quality development at the earliest juncture possible.

The model is grounded in China’s green building practices, which makes the conclusions drawn more specific. Furthermore, research results provide practical references for governments to formulate green building incentive policies.

Resilience concepts in integrated urban transport refer to the performance of dealing with external shock and the ability to continue to provide transportation services of all modes. A robust transportation resilience is a goal in pursuing transportation sustainability. Under this specified context, while before the perturbations, robustness refers to the degree of the system’s capability of functioning according to its design specifications on integrated modes and routes, redundancy is the degree of duplication of traffic routes and alternative modes to maintain persistency of service in case of perturbations. While after the perturbations, resourcefulness refers to the capacity to identify operational problems in the system, prioritize interventions and mobilize necessary material/ human resources to recover all the routes and modes, rapidity is the speed of complete recovery of all modes and traffic routes in the urban area. These “4R” are the most critical components of urban integrated resilience.

The trends of transportation resilience's connotation, metrics and strategies are summarized from the literature. A framework is introduced on both qualitative characteristics and quantitative metrics of transportation resilience. Using both model-based and mode-free methodologies that measure resilience in attributes, topology and system performance provides a benchmark for evaluating the mechanism of resilience changes during the perturbation. Correspondingly, different pre-perturbation and post-perturbation strategies for enhancing resilience under multi-mode scenarios are reviewed and summarized.

Cyber-physic transportation system (CPS) is a more targeted solution to resilience issues in transportation. A well-designed CPS can be applied to improve transport resilience facing different perturbations. The CPS ensures the independence and integrity of every child element within each functional zone while reacting rapidly.

This paper provides a more comprehensive understanding of transportation resilience in terms of integrated urban transport. The fundamental characteristics and strategies for resilience are summarized and elaborated. As little research has shed light on the resilience concepts in integrated urban transport, the findings from this paper point out the development trend of a resilient transportation system for digital and data-driven management.