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In the construction industry, various parties are involved in a project. Consequently, claims and disputes are inevitable in this industry. This paper aims to develop Integrated project delivery (IPD) practices including early involvement of stakeholders and multiparty contracts which its combination with advanced technologies such as blockchain can lead to better dispute management and improve the whole construction process.

Based on literature review, the alternative dispute resolution (ADR) for IPD contacts were identified, and three formats of IPD contracts were selected, and the dispute resolution process of them has been analyzed. Then, based on blockchain review, a conceptual blockchain-based dispute management (BDM) model was generated for ADR in IPD. Model validation was done by an interview. Experts were asked to compare the BDM model with the traditional system regarding the ADR duration.

Analyses of the collected data from the experts demonstrated that the BDM model has better function in terms of time and cost for ADR process when the project is facing serious and considerable number of disputes. The relation between blockchain technology (BCT) and building information modeling (BIM) has been examined through a framework, and the ability of the proposed model for administrating dispute resolution process has been verified using four different scenarios of construction claims that show the system can run successfully.

The current study proposes a truthful model, reliable framework to address the problem of project dispute management in IPD contracts. The system combines the ability to being unchangeable and the reliability characteristics of BCT with informative and automation aspects of BIM together to improve dispute resolution issue in the IPD system.

Planning phase of a project results to series of crucial decisions which determine the path to objectives achievement. At the same time, in this phase, project encounters the highest level of uncertainty in comparison of all phases of project lifecycle. This paper aims to support early decisions of project based on the progress forecasting.

The scope of study is limited to downstream projects of petroleum industry in Iran, and the proposed model is trained and tested based on 75 Iranian completed petroleum projects. First, types of progress curve functions are investigated, and various types are studied and the most appropriate ones are selected through curve fitting. In the next step, using questionnaire, dependent and independent variables are recognized. Finally, using historical data and s-curve generator functions, a fuzzy inference system (ANFIS) based model have been developed to support early phases decision-making processes.

Based on the analysis of received questionnaires, six functional criteria in two groups as dependent variables and 25 independent variables, in two groups and four clusters are determined and categorized. Eventually, performance prediction model of a project has been developed by using Adaptive Nero Fuzzy Inference System.

The main contribution of this study to construction management knowledge is categorizing two groups of variables, which first one defines the project dynamic and the other calculates the key effects on previous one. Also, this investigation improves the current knowledge by analyzing the project system from the dynamic behavior perspective and modeling the defined variables using ANFIS tools.

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.

The purpose of this study is to introduce and evaluate the effect of critical success factors (CSF) in rescue operations in burning buildings by calculating the partial least squares structural equation modeling of PLS-SEM.

To do this, success criteria (SC) and CSF in the literature, which are related to the topic, articles, standards and relevant books, will be identified and then evaluated through the extended PLS-SEM model.

The results show that technological factors, awareness, resources and safety play an effective role in successful performance management in fire accidents.

Appropriate use of these factors will promote incident management and decrease casualties and financial loss in the event of accidents.

Fire-fighting is of great importance, especially in tall and complex buildings. In recent years, extended studies have been carried out regarding fire accident management in terms of CSFs in the category of rescue and firefighting. However, attention has not been paid to the relation and severity of impact between SC and CSF by researchers in addition to the identification of the most important criteria during rescue operations.

This paper aims to combine Eringen’s micromorphic and nonlocal theories and thus develop a comprehensive size-dependent beam model capable of capturing the effects of micro-rotational/stretch/shear degrees of freedom of material particles and nonlocality simultaneously.

To consider nonlocal influences, both integral (original) and differential versions of Eringen’s nonlocal theory are used. Accordingly, integral nonlocal-micromorphic and differential nonlocal-micromorphic beam models are formulated using matrix-vector relations, which are suitable for implementing in numerical approaches. A finite element (FE) formulation is also provided to solve the obtained equilibrium equations in the variational form. Timoshenko micro-/nano-beams with different boundary conditions are selected as the problem under study whose static bending is addressed.

It was shown that the paradox related to the clamped-free beam is resolved by the present integral nonlocal-micromorphic model. It was also indicated that the nonlocal effect captured by the integral model is more pronounced than that by its differential counterpart. Moreover, it was revealed that by the present approach, the softening and hardening effects, respectively, originated from the nonlocal and micromorphic theories can be considered simultaneously.

Developing a hybrid size-dependent Timoshenko beam model including micromorphic and nonlocal effects. Considering the nonlocal effect based on both Eringen’s integral and differential models proposing an FE approach to solve the bending problem, and resolving the paradox related to nanocantilever.

This study proposes a model that influences customer citizenship behavior during COVID-19, and empirically tests the effects of fast-food restaurant customers' perceptions of justice (price and procedural justice) on trust; trust on satisfaction and loyalty; and trust, satisfaction and loyalty on customer citizenship behavior. Furthermore, it was questioned whether there was a disparity between customer expectations based on the restaurant's image and consumption experience.

The data were gathered from customers of fast-food restaurants in the shopping centers in Turkey. The data set, which included 437 valid questionnaires, was subjected to CFA for validity and reliability, SEM analysis for hypothesis and paired sample t-Tests for the research questions.

The findings of the study indicate that perceived justice affects customer trust, which, consequently, affects customer loyalty and satisfaction during the COVID-19 period. Findings also demonstrate that, while customer loyalty and trust increase customer citizenship behavior, customer satisfaction alone is insufficient to increase customer citizenship behavior. The study also shows that during the COVID-19 period, fast-food restaurants should have raised awareness of employees’ fair behaviors toward the customers and provided additional services to differentiate themselves in the market. Also, it indicates that customer expectations related to price, cleanliness and professional appearance of staff are not met after taking service.

No research has been found in the literature focusing on the expectations, justice, trust, satisfaction, loyalty and citizenship behaviors of fast-food restaurant customers in the COVID-19 pandemic process. Therefore, the results can fill the gap in relevant literature by testing the relationships between justice, trust, satisfaction, loyalty and citizenship during the pandemic and provide inferences for fast-food business owners.