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The mechanical facilities of large-scale buildings have many complexities, so facility management (FM) encounters a massive amount of information during the operation, and inspection is not implemented efficiently. This study aims to integrate building information modeling (BIM), augmented reality (AR) and sensors to mitigate the operation and maintenance (O&M) problems of mechanical facilities by establishing intelligent management.

The component-level data of an under-construction commercial complex were implemented in a 3D model in Autodesk Revit and Navisworks. Then, sensors were installed in mechanical facilities to provide vital online information for the model. Moreover, Unity was used on Vuforia for the AR visualization of the devices.

It was found that FM not only could obtain building information but can also visualize the functioning of mechanical facilities online through a 3D model in BIM. Thus, in the case of a failure, the AR platform is used on tablets, smartphones and smart glasses to show the technician how mechanical facilities can be repaired and maintained.

Previous studies focused on some factors and processes to improve mechanical FM and did not cover the entire aspect. However, this method reduces the average maintenance time, extends the lives of mechanical devices and prevents unpredicted failures.

The existence of hidden and dangerous points after the fire leads to loss of command of the rescue teams, casualties, and financial loss. This paper aims to identify these points by taking into account their contributing factors that can help to better manage, prevent and prepare for the risks even after their occurrence.

In this study, the fire overhaul was investigated by identifying the risks of this stage and ranking them in terms of impact. This paper introduces danger points using empirical knowledge (questionnaire) along with theoretical knowledge (literature review) and suggested solutions. Finally, by investigating the dangers and their effects on each other, it introduced three main danger paths. The main problem is the lack of applied knowledge, the relationship between theoretical knowledge at this stage of fire and its application in firefighting operations. To solve this problem, a model was developed to determine the relationships between the effects on firefighters’ health during fire overhaul using structural equation modeling (SEM) in the building. To develop this model, dangerous points were first identified, categorized and weighted, and then the effect of each of these points and their interactions was determined.

This paper contributes to the literature by identifying hazardous points and investigating their impacts on firefighters’ health using the SEM method. This study identifies 17 points that can be traversed through three paths.

Hazardous points could affect the success of the fire overhaul operation. Adopting a flexible model during this stage can enhance safety.

The results of this study can be used to rank and classify fire overhaul hazards in buildings and to increase firefighters’ safety and other related factors.

This study aims at introducing a claim management model based on building information modeling (BIM) for claims that can be visualized in BIM models.

Based on the results of a questionnaire survey, 10 claims were identified as claims that can be visualized in BIM models (named hard claims in this study). Then, a BIM-based claim management model was developed and used in a case study.

A BIM-based claim management model is represented. The claim management process through this model consists of four steps: (1) extracting project information, identifying conditions prone to claim and storing them into a relational database, (2) automatically connecting the database to building information model, (3) simulation of the claims in building information model and (4) final calculations and report.

The proposed model can provide benefits to parties involved in a claim, such as early identification of potential claims, large space for data storage, facilitated claim management processes, information consistency and improved collaboration.

There are a few studies on providing solutions to claim management based on BIM process. Hence, the original contribution of this paper is the attempt to set a link between BIM and claim management processes.

This paper aims to develop a decision-making model to analyze and select the most appropriate lean technique (LT), considering success factors to improve site management and reduce waste in the construction phase.

A comprehensive literature review identified six LTs and the 11 most critical factors affecting the efficiency of the construction phase. Then, the appropriateness of these LTs to improve the identified factors were assessed using the analytic hierarchy process. At last, a sensitivity analysis of the results was performed to validate the findings.

The results show that the most effective LT to improve success factors in the construction phase is “Daily Huddle Meetings”, and the most critical factor with the greatest impact on LT selection is determined to be “Efficient Construction Management”.

Few studies have evaluated the suitability of LTs in terms of construction success factors (CSFs). Hence, given the vital role of the construction phase in the whole project life cycle and the impact of LTs in improving the productivity of this phase, this research investigates the correlation between LTs and CSFs to promote efficient site management.

This research has been conducted with a view to creating a framework to integrate risk management based on building information modeling (BIM) information.

In this research, all the information related to the construction of a residential project including 3D, 4D and 5D BIM models and the execution and control phases information was collected, and the risk list was determined for each activity accordingly.

The present study has suggested a framework for risk management in order to optimize project changes.

The lack of integration between 3D, 4D and 5D modeling besides execution information is a fundamental problem in many projects. The gap between these two groups of information will lead to improper management and late decisions, eventually imposing unforeseen delays and cost overruns. Risk management by the means of adopting a new approach has been addressed in recent studies using new methods, such as BIM and its associated technologies, some of which were mentioned in the review of theoretical literature in this research.

This paper aims to increase the integrity of the planning process with the executive process by simulating the Last Planner System (LPS) technique using building information modeling.

A five-dimensional model was prepared, and after specifying the number of physical conflicts on the plans, all the scenarios of the starting points in the expert contractors’ plans were analyzed. Accordingly, the best scenario was determined by LPS technique. To assess the performance of the study, it was implemented on several tasks of a construction project.

The results of this research indicate that if this method is used in the planning process, the time conflicts between the expert contractors who are in the critical path are reduced, and the total delay of the scheduled time reaches zero. In addition, the conflicts between contractors whose suspension will cause no incentive and increase the daily cost overruns will reduce to the lowest point.

By simulating LPS in the planning process, time-physical conflicts are considered at the lowest point. Using this approach, conflicts are detected before the execution process, resulting in a significant reduction in the delays and cost overruns in construction projects.

Merging and updating project information and recording changes can give dynamic risk identification at all stages of the project. The main purpose of this research is to create an integration in construction information.

In this research, the 5D model was prepared and then all model information was entered into the database designed in SQL Server, the project report tables were coded, and finally, a database with four groups of information was ready for risk identification.

Creating an integrated risk identification platform reduced rework and time and cost control and change management, which were positive effects of risk identification at the right time.

In order to identify risks, creating multilateral databases whose information integration enables timely completion of the project and compliance with the planning.

This research is the basis for identifying project risks within the framework of building information modeling and can be an effective contribution to increasing the risk-taking efficiency of the project.

As a matter of fact, marked time and cost are terrific motivating forces for the building industry, materializing with identify risk well-time. In any case, identifying risk engaged with all of the dimensions depend on this industry.

The purpose of this research is to identify the effective criteria and determine the required infrastructure for the repair of drilling rigs.

The research was carried out through questionnaires and interviews with experts, including consultants, contractors and operators. A long list of the required infrastructure, dividing into three groups of vital, necessary and support, was prepared and compared with the productivity criteria.

The results show that it is better to prepare and upgrade the vital, necessary and supportive equipment in the shipyards, including refrigeration workshop, cofferdam for repairing spudcans and residential and educational places.

These results are helpful for the industries working on jack-ups and the countries having similar conditions to enable them to use their assets in an optimum way.

Oil drilling rigs' repair and maintenance (R&M) services are essential because of affecting the economy. Furthermore, about 25% of Iran’s oil drilling rigs are jack-up, needing overhauls every five years. Other research has investigated how to repair jack-ups, their incidents, their R&M industry market and, only in some cases mentioned, cost and time criteria. Therefore, it is necessary to identify the criteria and study the infrastructures in the shipyards.

This research aims to provide optimization and route safety planning employing the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique.

This research combines the use of graphical, communication tools and simulated models based on building information modeling (BIM) technology and agent-based modeling (ABM) to identify a safe evacuation route. Adopting the multi-criteria decision-making (MCDM) approach, the proposed rescue plan can reduce potential hazards along the evacuation route by selecting a safe route for evacuating residents and entering firefighters to the scene of the incident.

The results show that the use of simulated models along with MCDM methods in the selection of safe routes improves the performance of safe evacuation operations for both relief groups and residents.

The introduced model can improve the performance management of different groups at the time of the incident and reduce casualties and property losses using the information received from sensors at the scene. Moreover, the proposed rescue plan prevents group and individual reactivation at the time of the incident.

Despite many advances in the architecture, engineering and construction (AEC) industry, the number of victims of fire incidents in buildings is increasing compared to other natural disasters. Improving decision management based on effective parameters at the time of incident reduces casualties of residents and rescue workers.

Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.

The literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.

The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.

Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.

For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.