Identifying Design-Build Decision-Making Factors and Providing Future Research Guidelines: Social Network and Association Rule Analysis

There is a dire need to rebuild existing infrastructure with strategic and efficient methods. Design-build (DB) becomes a potential solution that provides fast-tracked delivery as a more time and cost-efficient project delivery method. Past research studied factors influencing DB but without providing a holistic analytic approach. This paper fills this knowledge gap. First, a systematic literature review is performed using the preferred reporting items for systematic reviews and meta-analyses techniques, and a set of factors affecting DB projects are then identified and clustered, using k-means clustering, based on the whole literature discussions. Second, a graph theory approach, social network analysis (SNA), is conducted methodically to detect the understudied factors. Third, the clustered factors are analyzed using association rule (AR) analysis to identify factors that have not been cross-examined together. To this end, the findings of this research highlighted the need to investigate a group of important understudied factors that affect DB decision-making and procedures that are related to management, decision-making and executive methods, and stakeholder and team related aspects, among others. Also, while the majority of the existing research focused on theoretical efforts, there is far less work associated with computational/mathematical approaches that develop actual DB frameworks. Accordingly, future research is recommended to tackle this critical need by developing models that can assess DB performance, success, and implementation, among other aspects. Furthermore, since none of the studies evaluated DB while factoring in all 34 identified relevant factors, it is recommended that future research simultaneously incorporates most, if not all, these factors to provide a well-rounded and comprehensive analysis for DB decision-making. In addition, future studies need to tackle broader sectors rather than focusing over and over on the already saturated ones. As such, this study consolidated past literature and critically used it to offer robust support for the advancement of DB knowledge within the construction industry

Advancing Airport Project Delivery: A Comparison Of Design-Build And Traditional Methods In Terms Of Schedule And Cost Performance

Current airport infrastructure is in a state of decline, with reports scoring it at an underperforming classification of D+. To address this issue, significant improvement and advancement of the infrastructure is needed. With backing on an authoritative level, the nation can expect an increase in the number of improvement projects. Airport stakeholders have long been accustomed to delivering their projects using traditional methods, such as design-bid-build (DBB). Design-build (DB) is an alternative delivery method that has added benefits for project metrics, such as schedule and cost performance. There is a lack of research evaluating DB within the context of airport projects. This study fills this knowledge gap. The goal of this research is to provide an improved understanding of DB with respect to DBB on fundamental key risks that impact schedule and cost performance in airport projects. This goal is achieved by a multistep interdependent methodology comprised of: (1) collecting and assessing data on 34 risk factors, (2) calculating the risk ratings of each factor, and (3) statistically analyzing the risks for their actual effect, as well as how they are perceived by between different stakeholder groups. The results show that the traditional DBB delivery method results in greater risks for most risk factors than does DB. Furthermore, contractors perceived DBB more negatively than DB. The top significant risk in DBB is the low level of team collaboration. Conversely, while statistically insignificant, unclarity or incompleteness in project scope was the most critical risk factor in affecting DB. To this end, DB implementation has promise for handling many risks better than DBB, and greater integration of DB should be prioritized in future airport projects to reap those added benefits. Ultimately, this research contributes to the body of knowledge by providing insight for airport stakeholders on the crucial risk factors that must be considered in project delivery

Automated Identification of Substantial Changes in Construction Projects of Airport Improvement Program: Machine Learning and Natural Language Processing Comparative Analysis

Contractual changes - mainly substantial changes - within airport improvement program (AIP) projects represent a critical risk that could result in severe negative time and cost impacts. It is critical for airport projects to have in place efficient procedures to process changes effectively, or otherwise this may create an administrative choke point for their stakeholders. Further, with the current US airport infrastructure scoring a D+ (i.e., lacking behind the general US infrastructure), associated authorities called for rebuilding the US airport infrastructure. Thus, it is expected that contractual changes are going to increase for current as well as future US airport projects. This makes it critical to identify these changes early on to incorporate proper change management strategies. However, analysis of contract documents is a process that is known to be inefficient, tedious, and prone to human error. The goal of this research is to create an automated framework to predict substantial contractual changes effectively and efficiently within AIP construction projects. An independent multistep research methodology was used based on principles of natural language processing (NLP) and machine learning techniques (ML). First, the authors adopted a data set containing 876 contractual changes made to the Federal Aviation Administration (FAA) document of guidelines and policies that govern AIP projects (FAA 5100.38D). Second, the authors used NLP techniques to preprocess the aforementioned data. Third, the authors developed hyperparameter-tuned ML models, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), artificial neural network (ANN), extreme gradient boosting (XGBoost), and logistic regression (LR) to predict substantial changes made to the FAA 5100.38D. Accordingly, results indicate that RF showed the most accurate prediction with an area under curve (AUC) value of 0.928, a testing accuracy of 87.45%, and a mean cross-validation accuracy of 92.67%. As such, this automated framework grants stakeholders associated with AIP construction projects a computational decision support tool to easily recognize substantial changes within contract documents, both efficiently and effectively. Ultimately, this research promotes better change management implementation and supports overall AIP project success

Construction Research Congress 2022

With an increasing demand for passenger and freight travel, airport construction program spending has heightened throughout the years. This poses a greater threat for sponsors to withstand the required financial capacities especially within the current economic conditions. None of the previous research attempted to provide a comprehensive overview on Airport Improvement Program (AIP) project expenditures. This paper fills this knowledge gaps using a graph theory approach. To this end, the authors used an interdependent research methodology that comprised of: (1) identifying AIP project keywords to represent the work actions performed; (2) vectorizing the data into a reference matrix that is split based on fund level; (3) developing the adjacency matrices from each reference matrix; (4) constructing the graph networks; and (5) visualizing the full data as well as interpreting for similarities and differences between the different fund levels. There were sustained similarities as well as differences between the fund levels. Results also indicated that the rehabilitation in the main aircraft entry, exit, maneuvering, and stationing areas are the main focus and center of all fund levels. Ultimately, this research synthesizes a foundation for comprehensive understanding of AIP projects by studying the interconnections as well as work actions performed in each fund level. This shall aid stakeholders in improved fund allocation decision-making procedures and management of human resources

Contract Risk Management: A Comparative Study of Risk Allocation in Exculpatory Clauses and Their Legal Treatment

Contracts used in the construction industry allocate numerous risks among the different contracting parties. The clauses of a contract that address risk allocation are often termed exculpatory clauses. Generally, exculpatory clauses shift the risk of injury, liability, and damages from one contracting party to the other. Previous research has concentrated on studying how exculpatory clauses influence the cost premium associated with the inherent stipulated risks in construction contracts without providing for how individual risks are allocated among project parties through exculpatory clauses and how their legal enforceability is determined. Accordingly, to fill this critical knowledge gap, this paper aims to offer a better understanding of the risk allocation process stipulated by exculpatory clauses under three commonly used US standard forms of the construction contract as well as their legal treatment under the US legal system. The forms under investigation are those published by the American Institute of Architects (AIA), ConsensusDocs, and the Engineers Joint Contract Documents Committee (EJCDC). The authors followed a multistep desktop methodology. First, the risk categories of exculpatory clauses were identified. Second, an analysis of the contractual allocation of the stipulated risks was performed on each standard contract. Third, law cases were considered in order to analyze the legal enforceability of exculpatory clauses in relation to common law principles. The outcomes of this paper include comparative tables that analyze exculpatory clauses as they relate to risk description, risk taker, and the risk response strategy provided by each standard form of construction contract. In addition, the results show that each contract allocates the same risks to different project parties when compared to the other contracts and that each contract possesses some specific risks not stipulated by the other forms. Ultimately, the findings of this paper protect the interests of contracting parties by helping them to proactively assess and manage their contractual risks

Evaluating Deterioration of Tunnels using Computational Machine Learning Algorithms

Tunnels are an integrated part of the transportation infrastructure. Structural evaluation and inspection of tunnels are vital tasks to assess the deterioration of tunnels and maintain their level of service. Researchers have developed many predictive models that describe the deterioration of various infrastructure systems using data from formal inspections. However, there is a lack of research that developed predictive models of deterioration of tunnels in the US. Therefore, this paper investigated the feasibility of using various machine learning techniques to develop a computational data-driven decision support tool that predicts the deterioration of tunnels in the US. An ex ante framework for predicting the deterioration of tunnels in the US was developed. The research methodology comprised (1) collecting, cleaning, and standardizing data for tunnels in the US from the Federal Highway Administration (FHWA); (2) identifying the best subset of variables that allow predicting the deterioration of tunnels; (3) utilizing existing machine learning algorithms, namely k-nearest neighbors (KNN), random forest (RF), artificial neural networks (ANNs), and support vector machine (SVM), to develop classification models that predict the deterioration of tunnels; (4) optimizing the accuracy of the developed models by determining the best set of hyperparameters that result in the most accurate performance; (5) comparing the performance of the developed models and selecting the best performing model to be used as a decision support tool; and (6) evaluating and validating the performance of the selected model. The results identified 18 variables that greatly affect the deterioration of tunnels, with the tunnel width having the greatest impact on the prediction of deterioration of tunnels. Results indicated that the RF algorithm reached an accuracy of 85.38%, which was the highest accuracy, compared with KNN, ANN, and SVM, which reached an accuracy of 80.12%, 56.14%, and 56.73%, respectively. In addition, the entropy criterion function with a maximum of five features and 500 estimators successfully constructed the best hyperparameters for the selected RF model. Therefore, the developed decision support tool can be used by transportation entities to estimate the overall condition of tunnels based on specific tunnel parameters with reasonable prediction accuracy. It also can aid decision makers in developing, optimizing, and prioritizing maintenance plans and allocation of funding

Managing Construction Projects Impacted by the COVID-19 Pandemic: A Contractual Perspective

The Coronavirus Disease (COVID-19) had severe, unforeseen, and prolonged implications on construction projects as well as many other industries. This resulted in severe effect on the supply of material, manufacturing, availability of human resources, and other factors which collectively negatively affected construction processes. To this end, there is a lack of comprehensive studies and understanding concerning that the contractual implications and remedies associated with the COVID-19 pandemic. This paper addresses this knowledge gap. The authors utilise a multi-step research methodology that comprised: (1) studying the contractual interpretation of COVID-19 under the American Institute of Architects (AIA) A-201-2017 as a widely used standard form of contract in the United States; (2) determining the associated contractual remedies for COVID-19; (3) comparing how the aforementioned two issues are handled under the Federation of International Construction Engineers (FIDIC) Red Book 2017, as being a much utilised international standard form of contract that is adopted by the World Bank; (4) developing guidelines and recommendations to be used by owners, contractors, project managers, and contract administrators in planning, handling, and mitigating the contractual implications of the current pandemic; (5) investigating the applicable legal doctrines and principles; and (6) solidifying the research steps and overall research outcomes using the input of legal experts who also helped investigate the relevant common law legal principles that could be associated with COVID-19. The results of this research should promote effective and efficient project management practices under the current new normal and similar interrelated conditions

Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis

Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two machine learning classification algorithms: Random Forest and XGBoost. Validation of the RF and XGBoost resulted in a balanced accuracy of 79.1% and 79.5%, respectively. Accordingly, XGBoost was selected. Testing of the XGBoost model revealed a balanced accuracy of 80.71%. Using SNA, it was found that while the top CEM subdisciplines in terms of the number of predicted impactful papers are “Project planning and design”, “Organizational issues”, and “Information technologies, robotics, and automation”; the lowest was “Legal and contractual issues”. This paper contributes to the body of knowledge by studying the citation level, strength, and interconnectivity between CEM subdisciplines as well as identifying areas more likely to result in highly cited publications

Graduate Recruitment Offers: Ethical and Professional Considerations for Engineering Graduate Students and Faculty Members

This paper investigates the ethical and professional responsibilities of engineering Graduate Students (GSs) and Faculty Members (FMs) in relation to Graduate Recruitment Offers (GROs). The authors developed an academic survey for data collection and subsequently evaluated the collected data based on common ethical theories and principles, as well as relevant professional codes of conduct. Based on the survey responses, this study identified the most common driving and preventive reasons for FMs and GSs not to honor a signed GRO. Further, the perception of GSs and FMs in relation to GROs was investigated using statistical methods. Finally, the authors provided an educational framework in the form of a checklist aimed at promoting ethical and professional decision-making as related to GROs. Ultimately, the outcomes of this research can be incorporated into senior seminar courses to enhance engineering undergraduate students\u27 ethical education and promote their ethical thinking as they grow into professional roles

Journal of Construction Management and Economics 40th Anniversary: Investigating Knowledge Structure and Evolution of Research Trends

Celebrating the Journal of Construction Management and Economics (CME) 40th anniversary, the goal of this paper is to investigate the knowledge structure and evolution of research trends in CME since its inception. The associated objectives include: (1) analyzing CME\u27s scholarly characteristics; (2) studying CME\u27s publication output over time; (3) examining interconnectivities between CME\u27s research trends; and (4) exploring the potential citation impact of recently published CME\u27s papers. In doing so, this paper implemented a multistep methodology that consists of descriptive assessment, social network analysis (SNA), and predictive machine learning (ML). Results of descriptive assessment showed that CME has witnessed over the years a noticeable growth in the number of publications, citation trends, and collaborative research as depicted increased co-authorship, and that highest percentage of publications were related to Strategy, Decision Making, Risk, and Finance , Project planning and Design and Contemporary Issues . Output of SNA highlights that research areas with the highest interconnectivity included Strategy, Decision Making, Risk and Finance and Project Planning and Design , and Labor and Personnel Issues . Furthermore, predictive ML revealed that CME papers have a high probability of becoming high impact publications. In addition to that, the predictive ML results re-emphasized the outcomes of the performed descriptive assessment by reflecting the importance of Contemporary Issues , Organizational Issues , Strategy, Decision Making, Risk, and Finance , and Labor and Personnel Issues as emerging research topics with increased potential impact in the future. Ultimately, this paper benefits all CME stakeholders by quantitatively studying current research patterns, their interconnectivities, and future potential