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PurposeMegaprojects 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.Design/methodology/approachThis study utilises a ground theory approach to analyse data gathered from semi-structured interviews with 18 professionals involved in 11 megaprojects.FindingsComplexity 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.Originality/valueThis 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.

PurposeMegaprojects 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.Design/methodology/approachRAMSCOM 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsDecision-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.Originality/valueThe 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.

Disputes, frequently encountered in construction projects, can substantially affect project success, necessitating a clear understanding of how and why disputes occur. Previous studies on disputes mostly yielded exhaustive lists or hierarchies of possible causes of disputes, which can hardly be used to understand how these causes come together to form a dispute. To address this gap, this study provides an alternative approach to understand the underlying causes of disputes, and their relationship within a specific context, using causal map analysis. This study is conducted using causal mapping approach to understand dispute emergence patterns in practice. Initially, a causal map of construction disputes is developed based on literature. The map is altered and verified through an expert workshop, considering projects contracted through FIDIC Yellow Book. The causal representation of the dispute emergence patterns highlights the importance of pre-construction studies, people factor, and contract terms. It is revealed that significant causes are either result of a chain of preceding factors or are triggers for further ones. This finding reinforces that the occurrence of disputes does not only depend on individual causes; rather, these causes combine with a series of other factors for a dispute to occur.

Risk information that is used during project risk identification and assessment should be communicated well to enable risk-informed decision-making. This study aims to use risk descriptors for risk contextualization and explore how visualization can improve the communication of project risk information. Risk descriptors (e.g., assumptions, controllability) were identified, and two workshops were held to verify the selected descriptors and explore the effectiveness of visualizations for risk communication. The first workshop was designed to assess the perceptions of different risk experts, and the second workshop was a case study application to evaluate the usability of risk visualization. Qualitative analysis of the first workshop revealed four themes, specifically standardization, representation, customization, and practicality, to be considered during risk visualization. The second workshop confirmed the value-added through the use of visualizations and the usefulness of risk descriptors. While this study does not focus on the best way of delivering the most useful data, it contributes to the existing body of knowledge by characterizing risk descriptors and introducing new insights regarding the use of visualization for communicating and describing risks in projects.

Although conceptualized in various ways within the project management literature, complexity is usually identified as one of the drivers of uncertainty, which may result in deviations from the expected project performance in the construction industry. Effective strategies to manage risk and complexity can only be formulated by understanding the complexity-uncertainty-performance triad in construction projects and modeling the non-linear interactions between these factors. In this study, a meta-modeling approach was utilized for understanding complex interactions within the triad. Using a database of projects from the seed scenarios developed by experts, a Bayesian Belief Network (BBN) meta-model was constructed for capturing complex interactions across the triad. Shapley value from Game Theory was adapted to assess the output of the BBN meta-model for establishing the relative contribution of complexity and uncertainty factors to project performance while considering the efficacy of potential strategies. The output of the Shapley value analysis was used to develop an Artificial Neural Network (ANN) model for predicting project performance. The application of the proposed methodology was demonstrated through a case study involving experts from an international construction company. The results clearly manifest the merits of integrating these approaches in a unique order in that BBNs are useful for understanding the holistic account of complex interdependencies involved in construction projects, utilization of Shapley value helps in establishing the relative importance of factors across a BBN meta-model and allocating limited resources to manage project complexity and uncertainty, and ANNs exhibit an improved accuracy in predicting the project performance given the relative importance established across complexity and uncertainty factors. The findings from the case study demonstrate that the proposed methodology can be used to understand how performance may change with respect to different combinations of implemented strategies, complexity and uncertainty factors.

Risk assessment in projects requires the integration of various information on project characteristics as well as external and internal sources of uncertainty and is based on assumptions about future and project vulnerability. Complexity is a major source of uncertainty that decreases the predictability of project outputs. In this research, the aim was to develop a decision-support tool that can estimate the level of risk and required contingency in a project by assessment of complexity factors as well as contextual information such as contract conditions and mitigation strategies. A process model and a tool were developed using the data of 11 mega construction projects. The tool was tested on a real project, and promising results were obtained about its usability. The tool has the potential to support decision-making during bidding in construction projects with its visualization and prediction features. On the other hand, as a limited number of cases and experts were involved in this study, findings on its performance cannot be generalized. The identified complexity and risk factors, proposed process model, and visual representations may help the development of similar decision-support tools according to different company needs.

Risk management is deemed as a critical process toward achieving the objectives of any project. A number of tools and techniques have been developed to help project managers reduce the risk involve...

In the globalized business world, construction companies start to seek new opportunities to invest on an international basis in order to gain profit. Therefore, construction companies have to condu...

During the feasibility study of BOT (Build-Operate-Transfer) hydropower investments, the selling price of energy is the most critical parameter that impacts the net present value (NPV) estimated by the investors. Investors usually consider the price of energy guaranteed by the government during their feasibility studies which is the worst case scenario. However, it is apparent that negotiations that take place between investor and broker determine the price of energy which is affected by various sources of uncertainty associated with the energy demand and country conditions. The objective of this study was to make a realistic estimate of the investor’s selling price by modeling the negotiation process between investor and broker using a multi-agent system (MAS). Thus, the factors affecting the negotiation process were identified, a negotiation protocol between the parties was set up, negotiation scenarios were determined, and modelled by using a MAS. The model was tested on a hydropower investment in Turkey and generated more realistic results compared to the current practice. Investors and brokers may benefit from this study because it considers the potential changes in the market as well as the negotiating postures of parties under different scenarios.

The construction industry is overwhelmed by an increasing number and severity of disputes. The primary objective of this research is to predict the occurrence of disputes by utilizing machine learning (ML) techniques on empirical data. For this reason, variables affecting dispute occurrence were identified from the literature, and a conceptual model was developed to depict the common factors. Based on the conceptual model, a questionnaire was designed to collect empirical data from experts. Chi-square tests were conducted to reveal the associations between input variables and dispute occurrence. Alternative classification techniques were tested, and support vector machine (SVM) classifiers achieved the best average accuracy (90.46%). Ensemble classifiers combining the tested classification techniques were developed for enhanced prediction performance. Experimental results showed that the best ensemble classifier, obtained from the majority voting technique, can achieve 91.11% average accuracy. Based on Chi-square tests, the most influential factors on dispute occurrence were found as variations and unexpected events in projects. Other important predictors were all related to the skills of the parties involved. This study contributes to the construction dispute domain in three ways: (1) by proposing a conceptual model that combined the diverse efforts in the literature for identifying variables affecting dispute occurrence; (2) by highlighting the influential factors, such as response rate and communication skills, as indicators for potential disputes; and (3) by providing an empirical ML-based model with enhanced prediction capabilities that can function as an early-warning mechanism for decision-makers.

Delays are almost inevitable in construction projects. They can have a devastating effects on the project. Therefore, proper analysis of delays and apportionment of liability is of utmost i...

Risk, complexity, and uncertainty are inherent components of megaprojects due to their unique features. However, existing project management practices lack a structured synthesis of these concepts, which leads to unrealistic risk assessments, ineffective management strategies, and poor project performance. In order to fill this gap, this research aims to develop a holistic risk quantification approach incorporating risk-related concepts. For this purpose, a conceptual risk assessment process developed for mega construction projects was operationalized with an Analytic Network Process (ANP) model. The weights of the risk sources in the ANP model were determined by the domain experts through a two-round Delphi study. With the purpose of improving the efficiency and reliability of the knowledge elicitation process, the Delphi study was supported by an interactive data collection tool capable of ANP calculations. The resulting model helped to prioritize the risk sources in mega construction projects. The validity of the findings was tested through the data of 11 mega construction projects. Validation studies revealed the potential of the ANP-based model in quantifying the project risks. Hence, the novel approach proposed in this study is expected to contribute both to the literature by unveiling the interactions between risk-related concepts and to the practitioners by assisting them in assessing the project risks more realistically. Although the risk quantification model has been developed for mega construction projects, it can also be implemented in other project-based industries with minor modifications.

Organizational learning has been identified as a core competency for project-based companies with industry specific challenges. In this study, it is hypothesized that a web-based lessons learned management system may help construction companies to manage lessons learned effectively. A lessons learned management process model (LLMPM) was proposed for capturing and transferring knowledge across projects. Expert review meetings with participants from both academia and private sector were carried out to develop LLMPM. The identified requirements were actualized through generation of a web-based IT tool (LinCTool) with abilities of capturing, storing and disseminating lessons learned. Black-box testing was used to verify LinCTool and interviews were conducted with experts as final testing to evaluate its effectiveness and usability. Results show that LinCTool has a potential to enhance organizational learning in companies by assigning multiple users having different responsibilities/roles in the learning process, categorizing lessons learned using a taxonomy and retrieving lessons learned considering project similarities.

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Originality/value 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.

PurposeThe 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.Design/methodology/approachTheoretically 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.FindingsProject 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.Originality/valueThis 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 this paper, we present a computerized method as an add-in to MS Project™ for risk identification and estimation of delay. A delay analysis ontology is incorporated so that the users can identify causes of delay at the activity level and assess frequency and impact of risks using their previous experience, expertise and judgement. Testing and validation findings demonstrate that the software is easy to use and gives reliable results. Decision-makers can use this software during risk identification and planning phases to incorporate their judgment about potential risk sources that may lead to project delay. The outputs of the software make it easy for decision-makers to understand the level of delay risk at the activity level, explore how criticality of activities may change under different risk occurrence scenarios and estimate amount of delay. By utilizing the software, effective risk response and allocation strategies may be formulated to mitigate the delay risk and communicate delay risk information between various project participants.

The main goal of this study is to propose a safety performance index assessment tool to improve the construction safety. Formulation of the safety performance index of construction sites is achieved upon a validated multidimensional safety performance model. The contribution of this study could be summarized as incorporation of fuzzy set theory into structural equation modeling to develop a safety performance index assessment software tool. Case studies were conducted at 11 international construction sites and the results of their site safety performance indices were benchmarked. A short (simple) safety performance model was developed as an alternative to the full model (proposed model) to assess safety performance of construction sites. Results showed that short model predicts the safety performance with an acceptable accuracy and requires less time to complete. Finally, a safety performance index assessment software tool for construction sites was proposed by developing a site safety performance (SSP) application for mobile devices based on the validated multidimensional safety performance model. The paper attempts to numerically validate the influencing factors of construction safety with the help of a mobile device application. The paper also develops a mobile application tool to measure safety performance at any construction site.

Although the construction industry is critical for sustainability goals due to the high amount of waste it generates, its waste management performance is still not at the desired level. One of the main reasons for this can be attributed to the lack of enough knowledge of construction companies on the sources/reasons of waste and amount of waste. The major aim of this study was to develop a knowledge-based tool for capturing, storing, and disseminating waste-related knowledge for prefabricated steel construction projects. The developed tool can be used for waste estimation, monitoring, and minimization of all materials used during different phases of prefabricated steel structure projects based on the data captured from current and previous projects. One advantage of the tool is the integration of all processes of waste management, which have been usually considered independently in previous studies. Moreover, being a knowledge-based tool, it can increase awareness and learning ability of companies about waste and its management. Finally, although the process model of the tool is generic and can operate in alternative domains, the tool is applicable for only prefabricated steel projects. It is believed that the process model and the tool presented in this paper can be further customized for different project types and considering different company needs.

Delay is a common problem of the construction sector and it is one of the major reasons of claims between project participants. Systematic and reliable delay analysis is critical for successful management of claims. In this study, a delay analysis ontology is proposed that may facilitate development of databases, information sharing as well as retrieval for delay analysis within construction companies. A detailed literature review on construction delays has been carried out during the development of the ontology and it is evaluated by using five case studies. The delay analysis ontology may be used for different purposes especially to support decision-making during risk and claim management processes. It may enable companies to create their own databases, corporate memories and develop decision support systems for better analysis of delays.

Construction sector has a great potential to reduce total energy consumption through the techniques applied in sustainable projects. All over the world, policy makers have already realized the potential and begun setting some governmental goals. Since new regulations for achieving sustainability have been recently set to change the conventionally practiced administrative patterns in construction sector, the number of claims related to sustainability features of the construction projects has been growing ever since. Construction contracts are playing a critical role in preventing these types of legal and administrative risks that can lead to claims. As a result, these changes have caused a need for some alterations in industry's contractual practices. Since the alterations are new to the field, one can, however, find only a limited number of researches on studying and analyzing the possible regulatory risks associated with green projects and their consequent probable legal claims. As a result, the present research intends to fill this gap in the literature, not only by identifying such risk factors which may lead to claims in sustainable construction, but also by assessing the criticality of each factor on the basis of the opinions of industry professionals and analyzing these factors with the relative importance index method. The findings of this study aim to increase the level of awareness of construction professionals about such probable risks. By revealing the possible legal risks of sustainable projects, the study shall also assist various parties of a contract in drafting particular conditions accordingly, in order to prevent probable future claims.

Optimum solution of time-cost trade-off problem has significant importance since it provides the highest profit opportunity. For this reason, exact, heuristic, and meta-heuristic algorithms are adapted to obtain the optimum or near-optimum solution. However, heuristic algorithms may not always converge into the global optimum, while meta-heuristic algorithms require significant computation to converge into global optimum and exact methods are complex for construction planners to implement. Therefore, minimum cost-slope based fast converging network analysis algorithm, which provides optimum or near-optimum solutions, is proposed for discrete time-cost trade-off problem. The algorithm searches the global optimum through the feasible crashing options. Number of feasible crashing options increase tremendously in large projects. Therefore, an elimination algorithm is embedded to reduce the number of crashing options. The crashing option with the lowest unit crashing cost is executed and global optimum is searched by stepwise crashing. Tests on 18 and 63-Activity projects revealed that the network analysis algorithm converges to optimum or near-optimum solution by only one percent of the computational demand of meta-heuristic algorithms. Consequently, the proposed heuristic algorithm is a convenient optimization method for the solution of time-cost trade-off problem.

Investigation of market entry strategies is critical for the success of international contractors. Establishing partnerships is among the most effective vehicles of operating in international markets. The major objective of this paper is to analyze the partnership behavior of contractors in overseas projects. In this respect, social network analysis (SNA) was used to better understand the collaborative project networks in different markets and for projects of differing sizes. A database was developed based on the collaborative international construction projects where Turkish firms and their non-Turkish partners were involved. A total of 449 projects carried out in 46 countries were used for the analysis. The findings of the study suggest that contractors adopt different strategies depending on the market and project character­istics. The majority of the companies tend to remain in the same markets; they keep working with the same partners or choose local partners; and engage with multiple partners in more complex projects. This study is expected to help contractors reflect on their internationalization decisions and devise appropriate strategies to establish project networks. First published online:27 Jun 2016

Performance of the construction projects have been criticized for many years due to their low productivity rates and cost overruns as well as significant delays. Increasing number of dissatisfied customers compel practitioners to reform conventional practices of construction management. Lean construction has emerged as a result of these efforts in the industry. However, there are limited number of studies that demonstrate the practical benefits of lean construction applications. The aim of this paper is to untangle practical applications of lean construction and to reveal their benefits. A methodology was developed to compare the lean and non-lean scenarios of a residential building project by means of Monte Carlo simulation. Simulation models were generated through regular meetings held with construction planning experts. Research findings demonstrate that application of lean construction principles have a potential to improve the performance of projects by reducing both the total project duration and its expected variation. In addition, the study re­vealed that utilization of practical lean construction principles may result in a considerable amount of time reduction in wall construction and plastering activities of residential building projects. Although the findings of this study cannot be generalized, they are expected to encourage practitioners to adopt lean construction principles. First published online:24 May 2016

Expert witness reports, prepared with the aim of quantifying fault rates among parties, play an important role in a court's final decision. However, conflicting fault rates assigned by different expert witness boards lead to iterative objections raised by the related parties. This unfavorable situation mainly originates due to the subjectivity of expert judgments and unavailability of objective information about the causes of accidents. As a solution to this shortcoming, an expert system based on a rule-based system was developed for the quantification of fault rates in construction fall accidents. The aim of developing DsSafe is decreasing the subjectivity inherent in expert witness reports.Eighty-four inspection reports prepared by the official and authorized inspectors were examined and root causes of construction fall accidents in Turkey were identified. Using this information, an evaluation form was designed and submitted to the experts. Experts were asked to evaluate the importance level of the factors that govern fall accidents and determine the fault rates under different scenarios. Based on expert judgments, a rule-based expert system was developed. The accuracy and reliability of DsSafe were tested with real data as obtained from finalized court cases.DsSafe gives satisfactory results.

Timely completion of dam and hydroelectric power plant (HEPP) projects is indispensable for the countries constructing them due to their economic, political, and social impacts. Robust and ...

Arbitration is an increasingly popular alternative form of resolution for construction disputes. After the establishment of the Istanbul Arbitration Center, the question of whether internat...

In case of an emergency in a building, first responders need to know current blockages in the building (e.g., blocked passageways and exits) and safe evacuating paths so that the occupants can be guided to the unblocked exits and safe paths toward those exits. To automatically determine blockage levels at buildings, a system that fuses data from multiple sensors and video camera was proposed. A prototype was developed and tested on an experimental model of a pilot building's hallway. A series of damage tests were conducted on the hallway model and recorded by the sensors and the video camera. Individual performances of sensors and video camera were evaluated, and a decision tree method was used to fuse sensor and video camera data for estimating the level of blockage in the hallway for different damage combinations applied on building components. The results demonstrated the technical feasibility of the proposed system and the findings of the decision tree highlight that by using less number of sensors, a cost-effective configuration can be achieved. The estimated blockage information can be used to create a topological map of the damaged building, indicating safe paths toward available unblocked exits.

Knowledge management in project based industries is critical as in the case of construction industry. Besides being a project based industry, factors specific to construction projects also make it harder to capture knowledge. Construction projects are held in dispersed locations, by different parties involved, with variable objectives; all of which make capturing the information more difficult and knowledge management process more complex. This study presents a database structure and an information system that can be used to capture dispersed information in the form of “lessons learned” as they accumulate during a construction project. In this paper, both the database structure will be proposed as well as the process that “lessons learned” can be utilized in forthcoming projects. A prototype database is created and retrieval capabilities provided within the structure are tested with entry of cases from hypothetical projects. This process reinforced the suitability of the proposed structure for codification and retrieval of “lessons learned” from a construction project. Possible use of the database is exemplified with “lessons learned” extracted from the hypothetical projects. This structure is aimed to base an information system where capture and dissemination of “lessons learned” from projects will be improved by its use, and planning and decision making processes for forthcoming projects will be facilitated.

Energy demand has been increasing, but traditional sources of energy are depletable. New investments are needed in renewable energy production. Hydroelectric power plants are often considered a feasible renewable source of energy and are often organized as a public private partnerships (PPP). However, risk factors stemming from the macro environment as well as project conditions should be considered in performing feasibility studies. The objective of this study was to develop a method that can be used to predict the profitability of hydropower investments considering the relevant risk factors. To that end, a cash flow that represents the construction and operation period is set up, the risk fac­tors involved in such projects are identified, the impacts of these risk factors on the cash flow parameters are assessed, and Monte Carlo simulation is performed to estimate the net present value (NPV) of a hydropower investment. The proposed method was tested in a hydropower investment located in Turkey and generated credible results that could be of great benefit to potential investors operating in similar conditions. The primary contribution of this research is the creation of a method that allows investors to assess the profitability of a hydropower investment by using a stochastic approach.

Portfolio management comprises of identifying, prioritizing, authorizing, managing, and controlling projects, programs, and other related work to achieve specific strategic business objectives. Utilizing a knowledge-based portfolio management approach can be a critical success factor for construction companies. This research aims to present a taxonomy to facilitate the learning process within a knowledge-based project portfolio management system. The taxonomy is capable of codification and classification of lessons revealed during life cycle of projects to enhance their retrieval. Within this context, following a detailed literature review process, the taxonomy is structured under four main categories as "project", "process", "actor", and "resource”. Categories provided in the taxonomy enable tagging of the lessons learned according to the intended level of detail, facilitate retrieval and reuse of the lessons learned in forthcoming projects. In this paper, we will present the structure of the proposed taxonomy and discuss how it can be used to improve portfolio management in construction.

The main goal of this study is to examine the relationships between determinants of safety performance of construction sites. To achieve this goal, 168 observable variables and 16 latent dimensions that affect safety performance of construction sites were collected through extensive literature reviews, expert opinions, and face-to-face interviews. A questionnaire form was developed and administered to construction professionals as a data-collection tool about the observable variables and latent dimensions. A total of 180 responses were achieved through data collection. Relationships between observable variables and latent dimensions of the safety performance of construction sites were studied and a multidimensional safety performance model was proposed. The proposed multidimensional safety performance model was supported by structural equation modeling (SEM) analysis. Moreover, 16 latent dimensions with respect to their relative weights and recommendations for construction safety professionals are provided to improve the safety performance of construction sites. Scopus

Although construction is a project-based industry, management focus has recently shifted from projects to project portfolios to meet strategic objectives of companies that require holistic analysis of the projects undertaken. Dependencies between projects within a portfolio need to be taken into consideration since they may significantly affect the portfolio success with their combined effects. There are limited studies in the area of construction project portfolio management that investigate how the dependencies between projects can be handled. In this paper, a method is presented to calculate and visualize project dependencies to support decision making process within a portfolio management tool for construction projects. Within this context, a dependency map is proposed not only to identify different dependencies and their effects within the portfolio, but also to take into consideration the combined effect of dependencies. An illustrative case study is depicted in the paper to demonstrate how the proposed method works and how its results can be used to support decision-making during portfolio selection. (C) 2017 The Authors. Published by Elsevier B.V.

Effective risk assessment and management is critical for success in international construction projects. This paper proposes a knowledge-based risk mapping tool for systematically assessing risk-related variables that may lead to cost overrun in international markets. The tool uses an ontology that relates risk and vulnerability to cost overrun [1] and a novel risk-vulnerability assessment methodology [2] to estimate potential risk paths that may emerge in international construction projects. The tool has been developed in collaboration with an industrial partner, a construction management company that gives risk management consultancy services to Turkish contractors working in international markets. The tool has been designed by using the previous projects of the partner firm as test cases and preferences of company professionals are taken into account while determining the functions of the tool. As the reliability and usability of the tool significantly depend on the subjective evaluations of users about level of vulnerability and magnitude of potential risk events, a lessons learned database has been incorporated into the tool so that decision-makers may refer to risk event histories of previous projects to make estimations about forthcoming projects. In order to evaluate the usability of the tool, a usability test has been conducted by eight construction experts. Usability test results demonstrated that the tool can be utilized for prediction of probable risk paths and their impact on cost. Results of a case study, a building project in Serbia, have also been reported in this paper to demonstrate the functions and the performance of the tool. Although the usability and reliability test results are satisfactory, the tool should be seen as an initial platform which should be improved by increasing its “intelligence” by high quality and enough number of risk event histories, and also customized according to user preferences as well as company policies.

27th World Congress of the International-Project-Management-Association (IPMA) -- SEP 30-OCT 03, 2013 -- Dubrovnik, CROATIA, WOS: 000396742800003, As essential elements of buildings, roofs traditionally correspond to about 3-8% of total project cost. Despite this considerable share, in the literature there have been only a few researches that investigate roof types preferred by owners/designers and make a cost analysis to reveal cost differences between alternatives. Toward this aim, in this study, twelve types of roofs installed in construction projects in Turkey were first examined. In this context, practical answers of the following questions were investigated briefly: (i) how different are roof types from each other, (ii) by which criteria are roofs chosen, and (iii) which roof types are selected in which buildings (such as, housing and industrial). Among twelve roof types, reinforced concrete flat roofs (RCFR) and free standing wooden roofs (FSWR) were determined as the most used roofs in housing projects. Finally, a real-life building project was considered. Its roof plan and cross sections of above-mentioned two roofs were given, and their detailed measurements and cost estimations were made. As a result, in terms of initial investment cost, a RCFR which has similar insulation conditions with a FSWR was found to be 35.46% more inexpensive than a FSWR. In conclusion, designers/constructers can manage projects more efficiently by directing their clients towards a more inexpensive option. Thus, potential building owners can allocate lower project budgets by decreasing their roof costs. As a research implication, future studies can compare life cycle costs of these roofs, which will likely provide a broader perspective for better cost management practices. (C) 2014 The Authors. Published by Elsevier Ltd., Int Project Management Assoc

PurposeThe 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.Design/methodology/approachIn 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.FindingsThe 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.Originality/valueResearch 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.

Multiagent systems (MASs) consist of several autonomous entities, called agents, that interact with each other to either fulfill their own interests (competition) or reach a common goal (cooperation). In MASs, negotiation is the key form of interaction that enables groups of agents to arrive at a mutual agreement regarding some belief, goal, or plan. The aim of this study is to develop a MAS that simulates the negotiation process between parties (mainly contractor and client) about risk allocation and sharing of cost overruns in construction projects. The MAS was developed using three different negotiation protocols: time-dependent concession, Zeuthen's strategy, and Zeuthen's strategy with Bayesian learning. Sensitivity analysis was carried out to test the effect of reservation value, first-offer value, and negotiation protocol on the settlement amounts. The model was also tested on three hypothetical cases. Results show that it is mainly the reservation value that determines the settlement amount of the negotiation. The developed system can be used by decision makers to understand how the risks and associated costs are shared between parties under different scenarios. DOI: 10.1061/(ASCE)CP.1943-5487.0000218. (C) 2013 American Society of Civil Engineers.

The major challenges of project risk assessment may be attributed to the knowledge requirements for determining the level of uncertainty and the development of complex models to predict project outcomes. The duration–cost influence network is an adequate model that can be used for activity-based risk modeling. In this study, a scenario analysis feature is added to this model and a Web-based tool is developed that can be used to calculate risk-adjusted duration and cost, considering the risks affecting cost and duration of individual activities. Monte Carlo simulation is used to create scenarios, and the output of these scenarios is calculated by using the cost–duration influence system that simulates the vulnerability of the project to the identified risk factors. The Web-based tool can be used by decision makers during the preplanning stage of construction projects to estimate the cost and duration implications of various risk occurrence scenarios, determine contingencies, and prepare risk-manage...

Economic globalization is increasingly affecting both the construction industry and academia. It is changing the traditional roles of civil engineers and construction managers. Cross-cultural collaboration and communication skills, multinational team management skills, the ability to overcome the social challenges of geographically distributed teams, and familiarity with construction materials, standards, and methods of foreign countries are vital for modern construction professionals. However, the traditional skills and education style of engineers and construction managers do not equip them to successfully deal with such issues. This paper describes the experiences of a university course International Collaborative Construction Management that was developed to educate the next generation of civil engineers to be more internationally savvy. Throughout the three years that the course has been conducted to date, students in Turkey, the United States, Israel, and Brazil were grouped in multinational teams. ...