Journal of Construction Engineering and Management

This paper suggests the methodology to follow when conducting discrete-event simulation (DES) studies in construction engineering and management research. Emphasis is made on the steps that, due to the uniqueness of the construction environment, are particularly important yet are not discussed extensively in the general DES literature. Guidelines are provided to determine what aspects of a DES study demand a rigorous application of the theory depending on the purpose of the study. The paper concludes with the importance of properly understanding the probabilistic concepts upon which DES relies and on coupling this understanding with engineering judgment as a key for successful use of DES in construction research.

Many construction planning problems require optimizing multiple and conflicting project objectives such as minimizing construction time and cost while maximizing safety, quality, and sustainability. To enable the optimization of these construction problems, a number of research studies focused on developing multiobjective optimization algorithms (MOAs). The robustness of these algorithms needs further research to ensure an efficient and effective optimization of large-scale real-life construction problems. This paper presents a review of current research efforts in the field of construction multiobjective optimization and two case studies that illustrate methods for enhancing the robustness of MOAs. The first case study utilizes a multiobjective genetic algorithm (MOGA) and an analytical optimization algorithm to optimize the planning of postdisaster temporary housing projects. The second case study utilizes a MOGA and parallel computing to optimize the planning of construction resource utilization in large-scale infrastructure projects. The paper also presents practical recommendations based on the main findings of the analyzed case studies to enhance the robustness of multiobjective optimization in construction engineering and management.

Humans’ unique use of tools originated with their capability to observe, to link causes with effects and to conduct trial and error experiments. Experimental research today is still using these same basic elements albeit augmented by sophisticated tools and methods. Researchers conducting scientific experiments in the construction arena, however, face a “harsh” environment to work in. This paper draws from 20 years of experimental work in construction and engineering education. It provides a short historic background before discussing a framework useful to categorize the various ways researchers conducted experiments that provided meaningful results. Many examples are used to underscore the most important points.

The paper provides a review of empirical modeling and its application within construction engineering and management. The scope of application and trends in use of this approach are first assessed, and the potential for its further development is identified. This is followed by an examination of the key components of empirical modeling, namely: the structure and operation of the model and the scheme used in its development. The paper then provides a rigorous methodology that must be followed to ensure the validity and value of the end model, covering the steps: strategizing; data collation and assessment; model development; model evaluation and final selection; final validation; and implementation. The methodology is designed to cater for all forms of empirical modeling including the procedurally more demanding development algorithms that have become available in recent years, such as simulated evolution. Overall, the paper is designed to provide researchers embarking on an empirical modeling study with an overview of when it is appropriate to use this approach, what type of system to adopt, and how to ensure development of a successful end product.

In the field of construction engineering and management, archival data sets are not always as correct and consistent as it would be desirable. Between different sources that are studied, e.g., companies, they may differ in format or content and within them, they may still be incongruous and require substantial preparation. This makes examining theories and extracting trends from historic data more difficult than it is for carefully controlled experimental studies or for collecting new data. The purpose of this paper is not to review the regression models that the writers developed during their research, but to focus on the data preparation that had to be applied before those analyses. The objective is to outline various techniques that can be applied to archival data that are related to construction engineering and management to give researchers a set of best practices on data preparation that can assist them in gleaning truths from them.

Traditional construction research methods have enabled focused but narrow advances in our understanding of industry phenomena. While contributing new insight, these methods are often not adequate to enable understanding of the complex interactions that lead to many of the industry’s pervasive social and technical problems. One means of addressing these limitations is for the construction research community to complement prevalent quantitative and case study methodologies with qualitative theory-building methodologies, specifically ethnographic studies or those based on detailed and long-term observation of project environments. This paper identifies the critical need for theory-building methods and their methodological conventions, challenges, and opportunities. Specifically, the paper focuses on data collection in ethnographic studies and analysis of that data through the use of grounded theory. Through better understanding and more widespread use of theory-building methods, the construction research community can provide a needed complement to the current prevailing methods and greatly aid the maturation of our important field.

Gaining innovative and useful research findings concerning construction industry best practices requires an interaction and feedback mechanism between industry respondents and academia. Typical research methods such as surveys, source document reviews, and structured interviews will work, but suffer from barriers which can hamper results. Examples of these barriers include low response rates, asynchronous communication, time commitment of the researchers and respondents, access to project data, and travel costs. Structured workshops (research “charrettes”) are a unique and useful method for facilitating data collection between industry respondents and academic researchers. They combine the best tenets of surveys, interviews, and focus groups in an accelerated time frame. This paper will explain how these workshops provide a critical avenue for industry interaction. Characteristics leading to successful charrettes will be outlined. The paper will conclude by describing the benefits of these workshops to researchers including lessons learned from successful workshops.

The evolution of research in the area of construction engineering and management has moved toward the use of quantitative metrics and research methods for identifying and evaluating the impact of events or procedures on the construction process. While quantitative research methods can be very important for answering certain research questions, there are important reasons for adopting qualitative or mixed methodology studies to gain a better understanding of complex phenomena. This paper explores the use of observational studies and their potential use within the field of construction research. Observational studies can provide answers to “what” phenomena occurred, particularly when people are involved in a process, along with gaining insights into “why” the phenomena occurred. This paper discusses two types of observational studies, structured and unstructured, and provides a procedure for their implementation within construction research. To clearly demonstrate the methodology, a case study focused on the investigation of two different versions of an educational simulation application, the virtual construction simulator (VCS), is used to illustrate the benefits and challenges of implementing mixed methodology observational studies. The case study involved the video recording and analysis of interactions between student team members when using the VCS application for a construction sequencing task. The video recordings were analyzed, and important insights were identified, both qualitative and quantitative. Through content analysis, it was determined that the improvements made in a new version of the VCS application were beneficial, and the detailed observational studies identified insights into why the revisions in the application yielded improved results. This case study details the steps and considerations involved in planning an observational study, as well as the benefits and challenges that researchers may encounter when using observational research methodologies.

Academic research in applied disciplines such as construction engineering and management (CEM) has the dual mission of simultaneously contributing to the solution of practical problems and creating theoretical and conceptual knowledge. To do so, appropriate research approaches are needed. However, extant literature in the field has paid little attention to this issue and research methods used have been almost entirely either quantitative surveys or case studies. In this paper, action research (AR) is proposed as an answer to this knowledge gap. AR aims at building and testing theory within the context of solving an immediate practical problem in a real setting. The paper describes the underlying philosophy and application procedure of AR and highlights its strengths and weaknesses. Then, the applicability of the method to CEM is illustrated through a case study of improving access to information to support planning and decision making in a construction owner organization through designing and implementing a data warehouse. The findings indicate that AR is a reliable, structured, and rigorous research approach that is very useful for conducting applied research in construction and enabling academia to influence and improve construction industry practices. It can also effectively help to improve collaboration between academic researchers and industry practitioners in research and development projects.

Construction engineering and management (CEM) researchers often rely on alternative research techniques when traditional methods fail. For example, surveys, interviews, and group-brainstorming techniques may not be appropriate for research that involves confounding factors and requires access to sensitive data. In such an environment, the Delphi technique allows researchers to obtain highly reliable data from certified experts through the use of strategically designed surveys. At present, the Delphi method has not seen widespread use in CEM research. This is likely due to variation among studies that implement Delphi in CEM research and ambiguity in literature that provides guidance for the specific parameters associated with the method. Using the guidance in this paper, the reader may: (1) understand the merits, appropriate application, and appropriate procedure of the traditional Delphi process; (2) identify and qualify potential expert panelists according to objective guidelines; (3) select the appropriate parameters of the study such as the number of panelists, number of rounds, type of feedback, and measure of consensus; (4) identify potential biases that may negatively impact the quality of the results; and (5) appropriately structure the surveys and conduct the process in such a way that bias is minimized or eliminated.

The fact that people play key roles in nearly all aspects of construction suggests that effective construction research requires proper application of social science research methods. This is particularly true for researchers studying topics that involve human actions or behavior in construction processes, such as leadership, innovation, and planning. In social science research, no single method of data collection (survey, experiment, participant observation, or unobtrusive research) is ideal. Each method has inherent strengths and weaknesses. Careful attention to the methodological ABCs of the design process, as discussed here, can enhance the validity and reliability of a given study. Combining quantitative and qualitative approaches in research design and data collection, however, should be considered whenever possible. Such mixed-methods research is more expensive than a single method approach, in terms of time, money, and energy, but improves the validity and reliability of the resulting data and strengthens causal inferences by providing the opportunity to observe data convergence or divergence in hypothesis testing.

An alternative approach to research is described that has been developed through a succession of significant construction management research projects. The approach follows the principles of iterative grounded theory, whereby researchers iterate between alternative theoretical frameworks and emergent empirical data. Of particular importance is an orientation toward mixing methods, thereby overcoming the existing tendency to dichotomize quantitative and qualitative approaches. The approach is positioned against the existing contested literature on grounded theory, and the possibility of engaging with empirical data in a “theory free” manner is discounted. Emphasis instead is given to the way in which researchers must be theoretically sensitive as a result of being steeped in relevant literatures. Knowledge of existing literatures therefore shapes the initial research design; but emergent empirical findings cause fresh theoretical perspectives to be mobilized. The advocated approach is further aligned with notions of knowledge coproduction and the underlying principles of contextualist research. It is this unique combination of ideas which characterizes the paper's contribution to the research methodology literature within the field of construction management. Examples are provided and consideration is given to the extent to which the emergent findings are generalizable beyond the specific context from which they are derived.

Validation of the research methodology and its results is a fundamental element of the process of scholarly endeavor. Approaches used for construction engineering and management research have included experiments, surveys and observational studies, modeling and simulation, theory building, case studies, and various subtypes thereof. Some studies use more than one approach. A particular contribution of this paper is that it reviews different types of validation using examples of studies, analyzes the specific challenges that were found to be significant, and presents how they were successfully overcome in each case. Another contribution is that it describes new opportunities for research validation that are emerging at the horizon as well as ongoing collaborative efforts to enhance the access of construction researchers to validation tools. This paper increases the awareness of the paramount role that validation techniques play in scholarly work by providing readers with recommendations to properly validate their own research efforts.