Assessment of Response Strategy in Mega Construction Projects

"Mega Construction Projects (MCPs) that are executed unavoidably face several of the organizational challenges and pressures. Due to the stakeholder pressures in the execution of MCPs, organizations may adopt various strategic responses. Purpose – The objective of this paper is to investigate the common response strategies (RSs) applied in MCPs in the State of Qatar, in addition to overcoming the construction problems and enhance performance during the construction stage. Design/methodology/approach – A questionnaire survey is carried out among the most important firms in construction industry in Qatar. Three steps are used to finalize and evaluate the questionnaire before proceeding with the full survey, validity, pre-testing and pilot study. Quantitative data analysis is carried through the Statistical Package for Social Science software (SPSS). Findings – Results define and demonstrate five different types of RSs. They are ranging from passive to active strategies determined by project organization. The RSs include: adaptation strategy, avoidance strategy, compromising strategy, dismissal strategy, and influence strategy. Originality/value – This paper identifies and evaluates the RSs in MCPs that could potentially improve project team more efficiently and effectively.

Stakeholder Management: An Insightful Overview of Issues

This paper attempts to contribute towards investigating the existing literature base of stakeholder management (SM), provide a compilation, and define any gaps in this area. Besides, explore different groups of critical success factors (CSFs) and grouping these actors. This study is based on reviewing the literature. Therefore, more than a hundred research papers were searched by the key terms specified in a preceding literature review. Succeeding tours of abstract research surveys resulted in forty-two research papers being picked for the compilation. SM constructs were identified, and the following crucial analysis defined the literature base gaps. The most notable outcomes are the absence of research that has studied BIM-based stakeholder management, especially in mega projects. Additionally, further investigations are still required to study the SM influence throughout the different stages of the project life cycle and study the impact of project type and contract type in SM. However, there is still considerable debate about the SM nature and merits approach. This study presents a comprehensive gathering of all earlier identified SM processes through a structured approach. Additionally, a more realistic and practical methodology for the development and implementation of SM will emerge, and twenty-seven CSFs associated with SM in construction projects are identified. The study is expected to have a theoretical contribution to this subject, especially in the context of the Qatari construction industry

Methodology for BIM implementation in the Kingdom of Saudi Arabia

Purpose The Architecture, Engineering, and Construction (AEC) industry is considered the most effective contributor to development in the Kingdom of Saudi Arabia (KSA). However, the AEC industry is facing myriad challenges due to the vast construction development required for the KSA 2030 vision. Developed countries are using Building Information Modeling (BIM) to mitigate these challenges and reap the benefits of implementing BIM to improve the performance of the AEC industry profoundly. However, BIM is currently rarely used in the KSA. This study aims to develop a methodology to implement BIM in the KSA by exploring stakeholders of factors affecting the implementation. Design/methodology/approach BIM users and non-users were surveyed by means of a questionnaire and structured interviews. The proposed methodology was validated through a further survey and structured interviews with BIM experts.Findings This study proposes a six-step methodology to implement BIM namely; raising awareness; perceived benefits; AEC industry readiness, and the key factors influencing the implementation. Practical implications The proposed methodology is expected to assist project participants in KSA to implement BIM to solve current AEC industry issues, improve performance and reap the benefits of implementing BIM.Originality/value This study makes a crucial and novel contribution by providing a new methodology to implement BIM in KSA that motivates decision makers and project players to adopt and implement BIM in their projects. It paves the way to develop BIM guidance and strategies

Practical approach for paving the way to motivate BIM non-users to adopt BIM

Typically, the Architecture, Engineering, and Construction (AEC) industry is considered one of the most effective contributor to the national developments worldwide. However, the AEC industry is facing myriad challenges due to the pressing calls for creativity and innovative solutions. Several issues are confronted such as failure to meet client satisfaction, delays in delivering projects on time, cost overruns, low quality, conflicts among parties, safety issues, increasing requests for change orders, tremendous increases in materials waste and project complexity. Building Information Modeling (BIM) is rapidly growing worldwide as a viable tool for improving the efficiency of the AEC industry to solve its salient issues. However, BIM is seldom adopted on the government level, especially in the developing countries. This study aims to explore the stakeholders' perceptions on the benefits of BIM and the barriers that hindered its adoption. Furthermore, practical solutions to motivate BIM non-users to adopt BIM are proposed. A questionnaire was sent to BIM users and non-users in the Kingdom of Saudi Arabia (KSA) as a case study. The key findings that deterred the implementation of BIM were personal correlated issues such as resistance to change and lack of appropriate awareness of BIM. This study convenes the industry players concerning BIM benefits and reveals the barriers and their potential solutions to encourage them to reap the benefits form BIM adoption

An approach based on Landsat images for shoreline monitoring to support integrated coastal management - a case study, Ezbet Elborg, Nile Delta, Egypt

Monitoring the dynamic behavior of shorelines is an essential factor for integrated coastal management (ICM). In this study, satellite-derived shorelines and corresponding eroded and accreted areas of coastal zones have been calculated and assessed for 15 km along the coasts of Ezbet Elborg, Nile Delta, Egypt. A developed approach is designed based on Landsat satellite images combined with GIS to estimate an accurate shoreline changes and study the effect of seawalls on it. Landsat images for the period from 1985 to 2018 are rectified and classified using Supported Vector Machines (SVMs) and then processed using ArcGIS to estimate the effectiveness of the seawall that was constructed in year 2000. Accuracy assessment results show that the SVMs improve images accuracy up to 92.62% and the detected shoreline by the proposed method is highly correlated (0.87) with RTK-GPS measurements. In addition, the shoreline change analysis presents that a dramatic erosion of 2.1 km2 east of Ezbet Elborg seawall has occurred. Also, the total accretion areas are equal to 4.40 km2 and 10.50 km2 in between 1985-and-2000 and 2000-and-2018, respectively, along the southeast side of the study area

Conceptual prediction of harbor sedimentation quantities using AI approaches to support integrated coastal structures management

Sedimentation is one of the most critical environmental issues facing harbors’ authorities that results in significant maintenance and dredging costs. Thus, it is essential to plan and manage the harbors in harmony with both the environmental and economic aspects to support Integrated Coastal Structures Management (ICSM). Harbors' layout and the permeability of protection structures like breakwaters affect the sediment transport within harbors’ basins. Using a multi-step relational research framework, this study aims to design a novel prediction model for estimating the sedimentation quantities in harbors through a comparative approach based on artificial intelligence (AI) algorithms. First, one hundred simulations for different harbor layouts and various breakwater characteristics were numerically performed using a coastal modeling system (CMS) for generating the dataset to train and validate the proposed AI-based models. Second, three AI approaches namely: Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN) were developed to predict sedimentation quantities. Third, a comparison between the developed models was conducted using quality assessment criteria to evaluate their performance and choose the best one. Fourth, a sensitivity analysis was performed to provide insights into the factors affecting sedimentation. Lastly, a decision support tool was developed to predict harbors' sedimentation quantities. Results showed that the ANN model outperforms other models with mean absolute percentage error (MAPE) equals 4%. Furthermore, sensitivity analysis demonstrated that the main breakwater inclination angle, porosity, and harbor basin width affect significantly sediment transport. This research makes a significant contribution to the management of coastal structures by developing an AI data-driven framework that is beneficial for harbors' authorities. Ultimately, the developed decision-support AI tool could be used to predict harbors' sedimentation quantities in an easy, cheap, accurate, and practical manner compared to physical modeling which is time-consuming and costly. © 202

Sustainable Building Optimization Model for Early-Stage Design

Buildings represent the largest potential for carbon reduction worldwide. This highlights the need for a simulation and optimization method for energy management. The early design stage of buildings represents an important phase in which choices can be made to optimize design parameters. These parameters can focus on multiple areas, including energy and thermal comfort. This paper introduces the optimization of early-stage sustainable building design considering end-user energy consumption. It proposes an optimization model that integrates multiple layers, which consist of a parametric energy simulation, artificial neural network, and genetic algorithm. The proposed optimization model considers a single objective function to obtain the optimal design. The targeted goal is to obtain minimal energy consumption for residential buildings during the early design stages. Key design parameters of the building were identified for optimization and feasible ranges for them were obtained using genetic algorithms. Finally, the results of this paper include the identification of the optimal building design for the thermal comfort analysis and optimal energy performance. The model was applied to a case study in Egypt and the results showed that using the developed optimization model can lead to a 25% reduction in energy consumption