A common cause of poor performance is that the design and construction process are particularly complex due to many reasons [9, 10]. There is no standard definition for project complexity that can be applied to a variety of projects. Baccarini [9] was the first to propose a definition of project complexity consisting of many varied interrelated parts. Gidado [11] defined project complexity as measuring the difficulty of implementing a planned workflow in relation to the project objectives. Despite the many existing studies on project complexity, there is no universal agreement on the definition of project complexity [12]. Remington and Pollack [1] referred to project complexity as feedback and evaluated interrelationships between vagueness and lack of certainty. Project complexity is the attribute of a project which makes it difficult to realize, forecast, and keep under control, despite having complete data regarding the project system [13].

Custovic [14] defines complexity as that feature of a system that makes it difficult to formulate its overall behavior in one language, even when given reasonable complete information about its atomic components and their interrelations. For instance, Bakhshi et al. [15] define project complexity as an intricate arrangement of the varied interrelated parts in which the elements can change and constantly evolve to affect the project objectives. Dao et al. [5] defined project complexity as the degree of differentiation of project elements, interrelatedness between project elements, and consequential impact on project decisions. Uncertainty can be defined as a future event or situation, which cannot be predicted with the available information, and it may have both positive and negative influences on the project outcomes [16]. However, there is still no commonly accepted definition of project complexity.

Generally, decision-making is the study of identifying and selecting alternatives based on the decision maker's values ​​and preferences. Making decisions implies the need to consider some of the reasons and choose the alternative that might best suit the objectives, goals, desires, and values ​​of the problem. Choosing the most appropriate multi-criteria methodology is in itself a multi-criteria choice [17]. Six criteria and 30 methods for this problem have been identified by Tecle [18]. Indeed, on evaluating the complexity of projects, preferences are given to the context of project management in terms of axioms, importance, notoriety, and sufficiency. Moreover, this method should be able not only to arrange the alternatives but also to suggest tables to ease the move to the computation of the complexity index.

There was a need for measuring the factors of complexity in order to manage project complexity efficiently. A lot of researchers have attempted to determine the weight of each factor of complexity and measure these factors [19-21]. However, characteristics of complexity do not all have the same negative impact and influence on the project's success [22], which makes it important to understand and measure the weight of each complexity parameter and its impact on the overall level of project complexity. The overall objective of the study presented in this paper is to develop a methodology to explore the complexity of the project and evaluate it completely. We have achieved this goal by (1) determining the complexity and characteristics, (2) identifying and testing the importance of the complex factors that influence the complexity of the project, (3) weighing the complex factors, (4) developing a proposed construction complexity index (CCI), and (5) providing guidelines on how to successfully manage the expected project complexity.

The ever-rising complexity of the project is the main source of project risk. Hence, identifying the sources and levels of complexity of projects has become a critical issue in order to aid in the management of modern projects. The main aim of this study is to develop an index of project complexity called Construction Complexity index (CCI) based on the previously identified relative weights of the input factors so that it can be used as an indicator, validate the developed index through some selected field case study applications, and provide guidelines on the management of various complexity factors. Moreover, this study adds to the information provided by previous studies regarding some of the guidelines directed to the participants involved in project management. The resulting assessment of project complexity adds significant value to researchers' existing body of knowledge and assists practitioners in allocating project resources to complex projects. From the perspective of complexity theory and complexity management, the research findings make a significant contribution to the theoretical foundation in the field of project management. This research develops an index by which the complexity of construction projects in Egypt can be measured and establishes guidelines on avoiding complexity in projects.

From previous studies, the AHP method was found to have the best results compared to other methods. The analytical hierarchy process was developed by Thomas Saaty [32-34]. It is a way of making multi-criteria decisions to allow the relative assessment and prioritization of alternatives. The AHP uses a decision problem model just as a hierarchy. It consists of a general target, a set of alternative elements, and a collection of criteria that link alternative elements to the target. In the context of the project's improved complexity, a hierarchical frame of the project is established. The general target is to rank alternatives according to their complexity level, which means that the degree of AHP that is eventually obtained compares the significance of complexity factors of each alternative. The prime level criteria (intermediate objectives) coincide with the four sets of complexity factors (people, project characteristics, process, and environment). The next level criteria then coincide with the factors of the polished frame, weighting the factors shown in Table 2.

All initial weights for each criterion and sub-criteria (factors) are summarized in Table 2.

Fig. (2) presents the weight percentages for the four main criteria and the weight percentages for each main factor related to specific criteria. The “people” criteria are the most effective criteria in the complexity index with a relative weight of 0.321. The “lack of leadership” is the most effective factor in people criteria with a relative weight of 0.282. The “lack of clear and detailed drawings and specifications” is the most effective factor in project characteristics criteria with the relative weight of 0.488. The “unpredictable subsurface (e.g., excavation in ancient city grounds)” is the most effective factor in the process criteria having a relative weight of 0.342. The” economic situation” is considered the most critical factor in environment criteria because it obtains a relative weight of 0.556.

Fig. (2). Percentage of the weighting of the four main criteria and percentage of the weight of each key factor related to specific criteria.

Factor weights developed from AHP have been used to make a composite indicator to evaluate the complexity of the construction project. Moreover, a Construction Complexity Index (CCI) has been developed, which can be represented in the following two formulas:

To evaluate a certain project complexity, the input factors in the second Eq. (2) have been replaced with their relevant scores according to Table 3.

The measure of relative project complexity is evaluated between 0 and 1 by evaluating factor sub-measures (this index allows projects to be classified in terms of complexity).

To check the validity of the construction complexity index, three projects were selected for the case study (Project 1, Project 2, and Project 3) in order to calculate the levels of complexity and assessment of the differences between the three projects mentioned. Table 4 shows details of the information on the mentioned case study projects.

Table 4 shows that all the case studies involved residential and industrial projects. The first project was to build a large administrative building in the administrative capital. The scope of the second project was to build a group of factories and mills. In addition to that, the third case study project was the construction of a group of residential buildings. Three case studies were selected to test the effectiveness of CCI. Table 5 shows the comparison of complexity factor scores for the case study.

In the penultimate phase, the results of the case studies were compared with the actual values of the completed projects, as shown in Table 6. The rate of the actual project time to the scheduled time was used as an indicator of complexity. Finally, the final results were obtained and allowed the projects to be ranked according to a complex scale/indicator (0 to 1), as shown in Fig. (3). It can be observed that project 2 was more complex than the other projects.

Fig. (3). The relative project complexity index in the case study.

To cope with the complexity of projects and improve project performance, the following dimensions must be considered in construction projects: leadership qualities, good management skills, effective communication skills, assurance, effectiveness, cognitive ability, competence, security, and integrity. To reach these dimensions, the following elements must be overcome: weak project managers, economic instability in the country, political interference in projects, various types of soil issues for projects, an increase in the number of tasks involved in the project, lack of internal organizational support for project activities, unclear project objectives, the scope of the project, and lack of expertise in the technology used to implement the project. From previous studies, it has been concluded that leadership competencies, managerial skills, communication skills, and effectiveness are higher-level solutions to the complexities of construction projects. It should be noted that complexity has multidimensional effects, so in order to counter this, it is necessary for a manager to have multiple properties to treat any of the factors that cause complexity to construction projects. The second part of this study aims to set guidelines on managing the previously identified complexity factors. According to Azim [35], guidelines previously found through literature are as follows:

(1) Recruitment of local consultants for engagement.

(2) Recruitment of professionals with more expertise in the technical field of the project.

(3) Determination of the senior management process in terms of obtaining funding.

(4) Incorporation of legal constraints into the project implementation plan.

(5) Understanding and documenting decision-making criteria and analytical requirements.

(6) Establishment of a well-defined and well-understood change management process, including approval time schedule and cost, impacts approval authorities.

(7) Making the timetable flexible to avoid too many critical paths.

(8) Review the best practices and lessons learned and develop a logistics plan at the conceptual stage of border sites.

(9) Recognition of productivity, existing schedules, estimates, etc., must be adapted to take account of new technology.

(10) Evaluation of subcontractors based on project size, experience, financial stability, and reputation.

(11) Establishment of an ad hoc project team to participate in all phases.

(12) Convergence of the objectives of the parties concerned is a critical strategic factor.

(13) Identification of scope, objectives, and initial requirements of the project before starting it.

(14) The unpredictability and certainty of the surrounding environment, the Earth's surface, and the Earth's layers, no matter how many tracts are implemented, should be considered as influencing the achievement of the scope, time, and cost of the project.