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Exact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.

Minimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.

The developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005.

In this study, TCTP is solved without considering quality or restrictions on the resources.

The proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.

The purpose of this paper is to address the limitations of conventional risk matrix based tools such that both positive and negative connotation of uncertainty could be captured within a unified framework that is capable of modeling the direction and strength of causal relationships across uncertainties and prioritizing project uncertainties as both threats and opportunities.

Theoretically grounded in the frameworks of Bayesian belief networks (BBNs) and interpretive structural modeling (ISM), this paper develops a structured process for assessing uncertainties in projects. The proposed process is demonstrated by a real application in the construction industry.

Project uncertainties must be prioritized on the basis of their network-wide propagation impact within a network setting of interacting threats and opportunities. Prioritization schemes neglecting interdependencies across project uncertainties might result in selecting sub-optimal strategies. Selection of strategies should focus on both identifying common cause uncertainty triggers and establishing the strength of interdependency between interconnected uncertainties.

This paper introduces a novel approach that integrates both facets of project uncertainties within a project uncertainty network so that decision makers can prioritize uncertainty factors considering the trade-off between threats and opportunities as well as their interactions. The ISM based development of the network structure helps in identifying common cause uncertainty triggers whereas the modeling of a BBN makes it possible to visualize the propagation impact of uncertainties within a network setting. Further, the proposed approach utilizes risk matrix data for project managers to be able to adopt this approach in practice. The proposed process can be used by practitioners while developing uncertainty management strategies, preparing risk management plans and formulating their contract strategy.

In the global construction industry, experience gained in various countries is a major source of competitive advantage. By transferring the knowledge gained in previous projects using an effective knowledge management strategy, they can increase their competitiveness by adopting best practices and by not repeating the same mistakes under similar conditions. The purpose of this paper is to demonstrate how similar countries can be clustered to facilitate learning in global markets.

Initially, country factors that can affect success in international projects and can be used to assess the similarity of markets were identified by an extensive literature review and prioritised by a web-based questionnaire. A country evaluation form was prepared to collect country-specific data to be used in the cluster analysis of 39 countries where the Turkish contractors have been frequently working since the last 45 years. Cluster analysis was performed with SPSS 23.0.

Three country clusters were obtained and validated. Ultimately, how obtained country clusters may be used to facilitate learning from international construction projects was demonstrated by using an illustrative example.

The findings depend on the experience and perspective of Turkish contractors doing business abroad. Thus, the identified clusters are not generic. Moreover, country clusters were not identified considering only the country factors such as economy, culture, politics, etc.; thus, countries in the same cluster do not necessarily represent “similar” countries in terms of macro-factors. Clusters were identified so that the lessons learned can be effectively transferred within the same cluster considering construction and project-related factors as well as country-related factors. Although the findings cannot be generalised and clusters are not static, it is believed that the methodology used in this research is repeatable for different countries considering different timeframes.

Theoretical contributions include the identification of factors that can be used for similarity assessment of countries for transferring lessons learned as well as a methodology for clustering. Findings may also have a practical value for the Turkish contractors.

Megaprojects are large-scale and long-term investments. Three pillars of sustainable construction objectives, namely, social, environmental and economic, should be integrated into megaproject risk management to ensure long-term success. A risk assessment method, Risk Assessment Method for Sustainable Construction Objectives in Megaprojects (RAMSCOM), was developed for this purpose.

RAMSCOM was developed based on the latest concepts and identifying relevant and critical sustainability objectives and risks through an extensive literature review. Analytical Hierarchy Process (AHP) and Cross-Impact Analysis (CIA) were used to determine and quantify the threats regarding the importance of the sustainability objectives. The applicability of RAMSCOM was demonstrated on a real megaproject.

The findings revealed that sustainability risk assessment requires integration of economic, environmental, social objectives and analysis of cross-impacts of risk factors. Visualization of interrelated threats, vulnerabilities and objectives has a potential to support risk mitigation decisions to achieve sustainability goals.

The method has been developed based on the findings from a detailed literature survey on sustainability objectives and risks. RAMSCOM was tested on a single project with the assistance of three experts' views. Findings from the case project cannot be directly generalised for various megaprojects considering the unique nature of megaprojects.

Decision-makers can use RAMSCOM to assess sustainability risks in megaprojects and develop risk management plans for the most vulnerable and important sustainable objectives in a visual and quantified approach to ensure megaproject's sustainability in the long-term.

The theoretical contribution is a novel risk assessment method that integrates all dimensions of sustainability and quantifies the vulnerability of sustainability objectives considering their priorities, interrelations and risks. Sustainability dimensions, objectives and risks used in RAMSCOM can be useful for other researchers aiming to develop similar methods.

Megaprojects are known as complex projects that involve high levels of uncertainty. This interpretive study explores and portrays perceived complexity in mega construction projects by lived experiences of project managers.

This study utilises a ground theory approach to analyse data gathered from semi-structured interviews with 18 professionals involved in 11 megaprojects.

Complexity in mega construction projects is defined as a project property that stems from the interaction of project features, uncertain variables/conditions, and managerial actions forming a pattern, which emerges over time, based on the reflections of construction practitioners.

This study defines complexity based on the reflections of the practitioners in the construction industry and uniquely identifies complexity patterns that may have implications for project management, particularly risk management.

The positive effect of differentiation strategy on achieving competitive advantage among construction companies is widely acknowledged in the literature. However, there exists only a limited number of studies in the construction management literature that investigate the drivers and modes of differentiation. The purpose of this study is to determine appropriate modes and drivers of differentiation strategy in the Turkish construction industry.

In this study, based on the results of an extensive literature survey, a set of modes and drivers are defined as well as a conceptual framework to investigate their interrelations. Structural equation modelling (SEM) is proposed as an effective tool to investigate the relations between various factors of differentiation and data regarding the Turkish contractors have been used to demonstrate its applicability.

The results demonstrate that construction companies can differentiate on two modes of differentiation, either on “quality and image” or “product variety and speed”. Among the identified drivers, project management capabilities influence “product variety and speed related differentiation” and “quality and image related differentiation” directly; corporate management affects “quality and image related differentiation” directly, whereas corporate management influences “product variety and speed differentiation” indirectly. “Resources” has a strong direct effect on project and corporate management, in addition “resources” influences “product variety and speed related differentiation” and “quality and image related differentiation” modes indirectly through corporate and project management.

Research findings may help companies to choose an appropriate differentiation mode considering their strengths and weaknesses related with the drivers. Although findings reflect the competitive environment prevailing in the Turkish construction industry, it is believed that the parameters identified in this study can further be used as a benchmark to conduct similar studies in other countries.

The paper seeks to identify the determinants of business failure in construction and to predict the failure likelihood of construction companies by assessing their current situation based on both company‐specific and external factors.

A conceptual model is designed based on an extensive literature survey. The analytical network process together with the Delphi method is utilised to compute the importance weights of variables on business failure through interviews and discussions with experts. The applicability of the proposed model is tested on five companies to estimate their failure likelihood by using the findings derived from the analysis.

The results suggest the importance of organisational and managerial factors, including the efficiency of the value chain at the corporate level, the appropriateness of organisational decisions, and the availability of intangible resources for the survival of construction companies.

The findings of the analysis are limited to the experiences of three professionals in the Turkish construction industry. The performance of the model is only tested in five companies. The accuracy of the model may be improved by using the diverse experiences of a larger group of experts.

The proposed tool may act as an early warning system for construction companies by estimating the level of their failure likelihood. Companies may benefit from the findings of the model to assess their current situations and take necessary action to avoid possible business failures.

The knowledge and experiences of experts are used to obtain a complete model that accommodates both external and company‐specific variables, and more importantly the inter‐relations among them. Similar models may also be developed for companies in other industries to diagnose their bankruptcy or failure likelihood.

The purpose of this paper is to investigate the role of exogenous factors in the strategic performance of construction companies. A conceptual model is proposed where strategic performance is influenced by a two‐dimensional construct composed of market conditions and strategic alliances.

A questionnaire survey was administered to 73 construction companies. Structural equation modeling was used to analyze the data and test the hypothesis that strategic performance is impacted by exogenous factors. The individual constructs used in the study passed the internal reliability test, all factor loadings were statistically significant at α=0.05, all goodness of fit indices consistently indicated a good fit, and the hypothesized path coefficient was large and significant at α=0.05.

The hypothesis was supported by the data and analysis. Indeed, macro‐economic, political, legal, socio‐cultural conditions and the level of competition and demand are expected to impact differentiation strategies, and market/project/partner selection strategies. The quality of the relationships with government agencies and clients is expected to influence client/project/market selection strategies, while the quality of the relationships with labor unions may affect the ability to differentiate by using innovative construction methods, materials and equipment.

It is likely that endogenous factors such as company resources, capabilities and project management competencies also impact strategic performance. But the study is confined only to the exogenous factors of market conditions and strategic alliances.

The findings of the study benefit construction company executives in that they make the executives more cognizant of the market environment and they draw the executives' attention to the importance of alliances with other parties. While market conditions are beyond the control of construction executives, relationships with other parties are somewhat within their sphere of influence.

Only a few studies have ever investigated non‐financial measures to assess the effectiveness of company strategies. Also, exogenous factors which are unavoidable in a project environment were also rarely discussed in the construction management literature. The originality of this study is that it uses non‐financial measures to assess the effects of exogenous factors on strategic performance.

For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field.

The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships.

The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART).

This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.