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The purpose of this paper is to present a hybrid simulation approach for predicting the value of labor productivity taking account of various continuous influencing factors and the interactions between different agents involved in the project.

The various continuous factors affecting labor productivity are simulated using system dynamics (SD). The heterogeneity of different agents involved in the project and their interactions is accounted using agent-based modelling (ABM). The developed ABM and SD models are finally integrated to simulate the value of labor productivity taking account of all the influencing factors.

The proposed hybrid simulation tool is implemented in a real project to evaluate its perfomance. The value of labor productivity is simulated by taking account of all the influencing factors. The most appropriate execution strategy is then selected using the developed hybrid SD-ABM approach to improve productivity. It is shown that the number of working groups and their movement patterns affect the severity of the groups’ interferences which will in turn affect the value of labor productivity.

This research helps project managers to predict and improve the value of labor productivity taking account of all the influencing factors.

It is believed that the proposed hybrid SD-ABM simulation approach offers a novel and robust tool for modeling labor productivity because the effects of various continuous influencing factors and the interactions between different agents are taken into account through the combination of SD and ABM. Many complex problems faced in construction projects involve interacting elements of a different nature, and the integration of SD with ideas from ABM offers potential to combine the strengths of the two methodologies to solve the problem.

This research aims to model the social dimension of sustainability in construction projects. A new hybrid system dynamic (SD)–fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is proposed to analyze the various factors affecting social aspect of sustainability taking into account their complex interactions.

The various factors affecting the social dimension of sustainable development are identified based on the opinions of 12 experts with an extensive experience in highway construction projects and a thorough knowledge and/or professional experience in the sustainability area. The qualitative model of social sustainability is constructed using SD approach and the complex inter-related structure of the various influencing factors are modeled using cause and effect feedback loops. Fuzzy set theory is applied to model the uncertainty of human judgments. The importance of various influencing factors is then determined quantitatively taking account of their complex interactions using the proposed SD-fuzzy DEMATEL method. The most significant influencing factors are finally determined.

To evaluate the performance of the proposed method, it is implemented on a real highway project and the importance of various factors affecting the social sustainability is determined. A set of complex interrelated factors affecting social sustainability are divided into cause and effect groups, and the root causes affecting the social sustainability performance of the project are determined. Therefore, the required managerial actions can be taken to improve the social sustainability.

Well-qualified experts with a well-developed mental model of social sustainability are necessary to provide required input data for modeling social sustainability using the proposed approach. The absence of such experts could be a limitation for the implementation of the proposed model on a new project.

The proposed Hybrid SD-fuzzy DEMATEL method provides a practical and robust tool to analyze the various factors affecting social sustainability taking into account their complex interactions.

The proposed method offers a more precise and accurate analysis of various factors affecting social sustainability of construction projects since the complex inter-related structure of influencing factors as well as the vague and imprecise nature of experts' judgment is taken into account efficiently.

This study aims to determine the optimal value of concession period length in combination with capital structure in build–operate–transfer (BOT) contracts, based on direct negotiation procurement and considering the conflicting financial interests of different parties involved in the project.

The financial model of a BOT project is developed considering all the influencing factors. Then, fuzzy set theory is used to take into account the existing risks and uncertainties. Bilateral bargaining game based on alternating-offers protocol is applied between the government and the sponsor to divide project financial benefit considering the lender’s requirements. Finally, concession period and equity level will be determined simultaneously according to the sponsor’s and government’s share of project financial benefit and the lender’s requirements.

The proposed model is implemented on a real case study, and a fair and efficient agreement on concession period length and capital structure is achieved between the government and the sponsor considering the lender’s requirements. It is revealed that being the first proposer in the bargaining process will affect the concession period length; however, it will not affect the equity level. Moreover, it is shown that considering income tax as a part of government’s financial benefit increases the length of concession period.

The presented model concentrates on direct negotiation procurement in BOT projects where the sponsor and government bargain on dividing financial benefits of project. It is assumed that the product/service price is determined before according to market analysis or users’ affordability. All the revenue of project during concession period is assumed to belong to the sponsor.

The proposed model provides a practical tool to aid BOT participants to reach a fair and efficient agreement on concession period and capital structure. This could prevent failing or prolonging the negotiation and costly renegotiation.

By investigation of previous studies, it is revealed that none of them can determine the optimal value of concession period length and capital structure simultaneously considering the BOT negotiation process and different financial interests of parties involved in the project. The proposed model presents a new approach to determine the financial variables considering the conflicting interests of involved parties. The other novelty aspects of the presented model are as follows: introducing a new approach for calculating the sponsor and the government’s share of project financial benefit that will affect the determination of the concession period length and considering the effect of existing risks and uncertainties on final agreement between the involved parties using fuzzy set theory.

Analyzing different scenarios at the design stage of construction projects has always been a challenging task. One of the main parameters that helps owners in making better decisions in designing their buildings is to look after the cost perspective on different design scenarios. Thus, this study aims to propose a semi-automated BIM-based cost estimation approach that enables practitioners to estimate the cost of projects based on different design scenarios by an accurate and agile system.

This study proposes an integrated framework, through which the cost estimation standard of Iran (FehrestBaha) is linked to the materials quantity take-offs (QTO) from BIM models. The performance of the system is based on connecting the classification standards of UniFormat and MasterFormat to the cost estimation standard of FehrestBaha. A BIM-based extension in the Revit environment is developed to automate the cost estimation process.

To evaluate the efficiency of the proposed approach in cost estimation, it is implemented to estimate the cost of the architectural discipline in a real construction project. The results indicate that the proposed BIM-based approach estimated the cost of the architectural discipline with an acceptable level of accuracy.

The proposed approach could be used by practitioners to have an agile and accurate BIM-based cost estimation of different scenarios during design process. The semi-automated system considerably reduces the time of cost estimation in comparison to the traditional manual approaches, particularly in complex structures. Owners are able to easily trace changes in project cost according to any changes in components and materials of the BIM model. Furthermore, the proposed approach provides a practical roadmap for BIM-based cost estimation based on cost estimation standards in different countries.

Unlike the traditional manual cost estimation approaches, the proposed BIM-based approach is not highly dependent on the knowledge of experienced estimators, which therefore facilitates its implementation. Furthermore, automating both QTO process and the required calculations in this approach increases the accuracy of cost estimation while decreasing the probability of human errors or omission occurrence.

Hospital heating, ventilation and air conditioning (HVAC) systems are essential to patient safety and wellness. System malfunctions, however, may result in energy waste and even pose health dangers. This project aims to provide a fault detection and diagnostics framework designed primarily for HVAC systems in hospitals.

In order to identify problems in hospital air handling units, the study uses a data-driven methodology that makes use of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) models. To address the problem of uneven data, the dataset is balanced. Other machine learning classifiers, such as Logistic Regression, Multilayer Perceptron, Support Vector Machine, Random Forest, Gradient Boosting and eXtreme Gradient Boosting, are compared to see how well the LSTM and GRU models perform.

Regarding defect detection, the LSTM and GRU models outperform traditional classifiers in terms of both accuracy and computation speed, with high accuracy rates surpassing 90%. Due to its simpler design, GRU achieves higher accuracy and performs faster calculations than LSTM. These recurrent models work well to identify temporal relationships in time-series data, which is crucial for detecting HVAC system problems.

This study closes a research gap by concentrating on issue identification in hospital HVAC systems using actual data. It illustrates how deep learning may increase the precision of fault identification and computational efficiency in medical settings by utilizing LSTM and GRU models.

Successful implementation of infrastructure projects has been a controversial issue in recent years, particularly in developing countries. This study aims to propose a decision support system (DSS) for the evaluation and prediction of project success while considering sustainability criteria.

To predict sustainable success factor, the study first developed its sustainable success factors and sustainable success criteria. These then formed a decision table. A rough set theory (RST) was then implemented for rules generation. The decision table was used as the input for the rough set, which returned a set of rules as the output. The generated rulesets were then filtered in fuzzy inference system (FIS), before serving as the basis for the DSS. The developed prediction tool was tested and validated by applying data from a real infrastructure project.

The results show that the developed rough set fuzzy method has strong ability in evaluation and prediction of the project success. Hence, the efficacy of the DSS is greatly related to the rule-based system, which applies RST to generate the rules and the result of the FIS was found to be valid via running a case study.

Use of DSS for predicting the sustainable success of the construction projects is gaining progressive interest. Integration of RST and FIS has also been advocated by the seminal literature in terms of developing robust rulesets for impeccable prediction. However, there is no preceding study adopting this integration for predicting project success from the sustainability perspective. The developed system in this study can serve as a tool to assist the decision-makers to dynamically evaluate and predict the success of their own projects based on different sustainability criteria throughout the project life cycle.

To address requirements and specifications of construction project, academics need to build a project classification model. In recent years, project success concept, particularly on large-scale construction projects, has been a controversial issue, especially in developing countries. Hence, in this paper, after introducing a sustainable success index (SSI), a novel method called “rough set approach” had been adopted to induce decision rules and to classify construction projects. The paper aims to discuss these issues.

At first, 20 effective success factors and 15 success criteria based on three pillars of sustainability of economy, society and environment had been categorized. The research data used for analysis had been collected from 26 large-scale construction projects in Iran and five other countries. After collecting data collection, observations had been analyzed and 51 decision rules were generated, and the projects were classified. Eventually, in order to evaluate the performance of the generated rules, confusion matrix was applied, and the model was validated.

The results of the present study show that rough set theory (RST) can be an effective and valuable tool for building expert systems. Practical applications of these results along with limitations and future research are described.

Perhaps for the first time, in the present study, a number of large-scale construction projects are classified based on SSI. Applying RST for building rule-based system and classifying projects in construction project area are novel attempts undertaken in this paper. The rules induced in this study can be applied to develop a sustainable success prediction model in the future studies.

It is a well-accepted note that to enhance safety performance in a project by preventing hazards, recognizing the safety leading indicators is of paramount importance.

In this research, the relationship between safety leading indicators is determined, and their impacts on the project are assessed and visualized throughout the time of the project in a proactive manner. Construction and safety experts are first interviewed to determine the most important safety leading indicators of the construction industry, and then the relationships that may exist between them are identified. Furthermore, a system dynamics model is generated using the interviews and integrated with an add-on developed on the building information modeling (BIM) platform. Finally, the impacts of the safety leading indicators on the project are calculated based on their time of occurrence, impact time and effective radius.

The add-on generates a heat-map that visualizes the impacts of the safety leading indicators on the project through time. Moreover, to assess the effectiveness of the developed tool, a case study is conducted on a station located on a water transfer line. In order to validate the results of the tool, a survey is also conducted from the project's staff and experts in the field. Previous studies have so far focused on active safety leading indicators that may result in a particular hazard, and the importance of the effects that safety leading indicators have on another is not considered. This study considers their effects on each other in a real-time manner.

Using this tool project's stakeholders and staff can identify the hazards proactively; hence, they can make the required decisions in advance to reduce the impact of associated events. Moreover, two other potentially contributions of the presented work can be enumerated as: firstly, the findings provide a knowledge framework of active safety leading indicators and their interactions for construction safety researchers who can go on to further study safety management. Secondly, the proposed framework contributes to encouragement of time-based location-based preventive strategies on construction sites.

As a pillar of integrated digital delivery (IDD), building information modeling demonstrates the tremendous potential to enhance productivity for the architectural, engineering and construction (AEC) industry worldwide. However, the implementation of digital solutions presents numerous challenges related to its adoption and implementation. Distinguishing a comprehensive set of critical factors can facilitate the construction professionals to execute their strategies in a properly planned manner, thus augmenting the possibilities of successfully implementing BIM in their organization. This study aims to identify critical success factors (CSFs) for BIM adoption and implementation in Singapore.

This study adopted structured empirical questionnaire survey. Relevant data were collected from the various stakeholders in Singapore AEC industry through an online survey questionnaire. Furthermore, data analysis was done using SPSS Statistics software in order to identify the key factors (KFs) based on which the CSFs were derived for BIM adoption and implementation during the construction phase.

From a set of 45 influencing factors, 35 KFs were derived after performing ranking analysis, from which a set of 26 CSFs were finally obtained based on the factor analysis methodology.

This study has identified the CSFs of BIM adoption in Singapore, as well as in the builders' perspective on how to enhance the digitalization in construction projects.

The Iranian construction industry has been grappling with numerous problems in recent years, including rework, high costs and design errors. Engineers in this field have always highlighted the use of modern technological methods of construction to improve quality and productivity and reduce time and cost. One of these technologies is the so-called building information modeling (BIM), which has been very difficult to adopt and implement in Iran. The purpose of this study is to propose a systemic and holistic model to analyze the dynamics of adoption and implementation of BIM in this country. The purpose of this paper is to understand the dynamics of BIM acceptance to identify the most effective policy to maximize it in the Iranian manufacturing industry.

A two-stage methodology has been developed to achieve the purpose of the research. In the first stage, a technology acceptance model for BIM acceptance was developed using the grounded theory (GT) method. This conceptual model provides a holistic basis for building a simulation model. Thus, in the second stage, we used the dynamics system methodology to extract a dynamic model from the conceptual one. This dynamic model can simulate different policies and may be used to evaluate their respective effectiveness.

In this study, using the GT method, we obtained 510 primary codes, 118 secondary codes, 50 concepts and 17 categories. After determining the relationships between categories through axial coding, we reached a conceptual model based on selective coding. Mention some of the variables of the conceptual model. Awareness, security, perceived usefulness and perceived ease of use are some of the most important variables of this model. In the next part, this conceptual model was run using system dynamics and, thus, turned into a causal model in which all the effective variables on BIM technology and their relationships with each other are specified. The stock and flow diagram of the problem and its related equations were presented. To improve the model and solve the problem, we examined the four policies as four future scenarios on the model: continuing the status quo, development of specialist workforce training, bolstering governmental support and increasing awareness via advertisement within. The simulation results showed that government support is the most effective policy for maximizing BIM acceptance in Iran.

In addition to enumerating all the factors affecting BIM technology, this paper proposes a systemic model that provides an accurate and comprehensive view of the acceptance of this technology. In this regard, by introducing feedback loops, as well as reinforcing and balancing factors versus factors causing stasis, the model offers a much deeper insight into mechanisms associated with BIM development and its barriers. Therefore, this study provides a very useful perspective and basis for policy-makers and all stakeholders to accept and implement BIM technology. The findings of this study can lead to more accurate policy-making, removal of acceptance barriers, promotion of incentives, and consequently more effective acceptance of BIM technology.

In this study, a new mixed research method was used. The innovation of our study lies in its simultaneous use of GT method to construct an accurate and holistic model and applying the system dynamics methodology to build a holistic and systemic model of the BIM acceptance problem. This research also provides a suitable standard and tool for studying BIM technology in developing countries.