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The ASCE Journal of Construction Engineering and Management has announced the recipients of the 2024 Best Paper Award. The winning article, titled "Sequential Machine Learning for Activity Sequence Prediction from Daily Work Report Data," discusses the development of a machine learning model that can accurately predict the sequence and overlap of critical activities in construction projects. The model achieved a reliability rate of 94.4% and utilized daily work reports as a data source, which is a commonly available but underutilized resource. The article was selected from a pool of 188 technical papers, 16 case studies, and 5 state-of-the-art reviews. [Extracted from the article]

The editor in chief of the Journal of Construction Engineering & Management will be stepping down from their position on September 30, 2024. They express gratitude for the opportunity to serve in this role since June 2011 and acknowledge the contributions of the Editorial Board and reviewers in maintaining scholarly quality. Dr. Carlos H. Caldas from the University of Texas at Austin will be taking over as the new editor in chief starting on October 1, 2024. The outgoing editor in chief also acknowledges the support of administrative staff and the ASCE Construction Institute. [Extracted from the article]

Distractions theory and general practice have suggested that providing safe physical conditions will reduce task demands and thereby increase workers' ability to develop highly productive and safe production systems. However, the construction industry has not achieved greater productivity and safety gains despite extensive safety efforts. This study aimed to examine whether the reduced task-demands as a result of safer conditions actually causes fall risks to be underestimated, encourages increased productivity, and changes risk-taking behaviors. To do so, the changes in study participants' productivity, risk perception (through physiological and subjective measures), risk-taking behavior (through tracking subject's motion, and localizing their positions and postures), and safety performance (through frequency of near misses) were examined when they were provided with various levels of safety interventions. The findings indicated that the reduced perceived risk and the desire for increased productivity may skew risk analysis and strongly bias workers toward presuming invulnerability when safety interventions are in place. Known as risk compensation cognitive bias, this change in human behavior counteracts the traditional outcomes explained by Hinze's distractions theory. The empirical evidence from a simulated roofing task helped substantiate the proposed productivity-safety model, which illustrates how safety interventions might become counterproductive because of the risk-compensation bias experienced by workers. [ABSTRACT FROM AUTHOR]

The Journal of Construction Engineering & Management has announced the recipients of the 2023 Best Paper Award. The winning paper, titled "Deep Learning Image Captioning in Construction Management: A Feasibility Study," explores the automation of deep learning for image captioning in the construction management field. The authors present a systematic approach that includes developing a linguistic schema, creating a dataset of manually captioned images, and evaluating six deep-learning methods using eight metrics. The paper concludes that a method combining two advanced deep learning processes performed the best, and that automatic captioning in construction management is feasible. The article provides valuable insights and suggestions for future development in this area. [Extracted from the article]

The pursuit of Faster Fast Track or Flash Track is deemed necessary in today’s industry for reasons such as emergency rebuilds, competitive market advantage, and regulatory compliance. Flash Tracking has been defined as a time-driven project, which by necessity requires a heightened degree of concurrency between engineering, procurement, and construction. This paper reports on Flash Track readiness assessment algorithms that were developed to enable an organization to assess its readiness to execute time-critical or Flash Track projects. The readiness assessment algorithms were developed by defining the relative importance of 47 essential Flash Track practices, grouped into 6 categories using the analytic hierarchy process (AHP). The weights of the practices and categories were determined by a 15-member industry expert panel, comprised of 15 Construction Industry Institute (CII) member companies representing owners, contractors, and engineering organizations. The algorithms were ultimately integrated into a Flash Track toolkit. In a validation exercise, representatives from 13 completed Flash Track projects used the toolkit with reference to their projects. Project representatives’ retrospective self-assessment of readiness to perform the projects on a Flash Track basis were moderately to strongly correlated (r=0.76) with their readiness assessment forecasted by this research’s algorithms. This research provides the first quantitative measurement suitable to assess an organization’s readiness to undertake the unique challenges of a Flash Track project. [ABSTRACT FROM AUTHOR]

Abstract: Infrastructure systems in the US are in urgent need of maintenance and rehabilitation. According to the most recent factsheet published by the American Society of Civil Engineers, one of the top five infrastructure concerns of today in the US is the transportation system. The major challenge facing maintenance managers in state Departments of Transportation (DOTs) today is to preserve the road networks at an acceptable level of serviceability subject to the stringent yearly maintenance and rehabilitation (M&R) budgets. Maintenance managers must allocate such limited budgets among competing alternatives. Absence of simpler decision-making tools exacerbates the matter. This paper presents the development and implementation of a simpler, yet useful, network-level pavement maintenance optimization model, which is a Linear Program (LP) subject to budget constraints and the agencies’ pavement performance goals in terms of total lane-miles in each pavement condition state. A decision-making tool is developed using Frontline Systems’ Risk Solver Platform add-in for Microsoft Office Excel. This decision-making tool can compute the optimal amount of investment for each pavement treatment type in a given funding period. Pavement condition data pertaining to one of the Districts within a state DOT is used to test the model presented herein. Within this context, nine treatment types along with their corresponding unit prices ($/Lane-Mile), five pavement condition states, pavement deterioration rates, network-level pavement performance targets, and available annual maintenance budget for a 15-year planning horizon are defined. The results presented show how an annual highway maintenance budget needs to be allocated or determined to achieve the District’s value proposition for various scenarios. Comparing the results of these varying scenarios provides insight on long-term strategies and the impact of target constraints on budget expenditures. [Copyright &y& Elsevier]

One of the prominent trends in business organizations today is the attention placed on individual personality traits as a means of predicting job performance. As such, the current study investigates the relationship of personality traits with critical success behaviors in the engineering and architectural professions’ project design services. The four project service categories measured are: conceptual design, contract documents, construction administration, and firm management duties. The measuremnt of the individual personalities is accomplished through the Myers-Briggs Type Indicator (MBTI). This research found that those possessing a preference for Intuitive data collection (MBTI, N) and Perceiving structure (MBTI, P) outperformed individuals with preferences for Sensing and Judging, in both planning and construction. However, professionals with a personality favoring Judging (MBTI, J) outperformed in the duties associated with the design phase. Contrary to predictions, the decision processes captured in the Thinking/Feeling dimension (MBTI, T/F) did not influence the performance in any of the four service categories. The implications of the results of this research are discussed. [ABSTRACT FROM AUTHOR]

Reports on a Decision Support System (DSS) for one district in the Commonwealth of Virginia developed through the application of System Dynamics concept. Architectural aspects of the DSS; Highway management system; Management information system; Results presentation system; Conclusions.

Focuses on the use of a parameter dependency network to represent design rationale in civil engineering. Possible data structure capable of generating a parameter dependency network; Computer program as a data verification assistant to the human designer; Research implications.

This paper focuses on applying that part of the design-process knowledge referred to as “design intent,” which is of essence to material selection and substitution. We have focused on the type of design intent that is related to architectural design. We express design intent in terms of function, user requirements, performance, and physical properties of a design description. This paper shows how design intent can guide the contractor's material selection and substitution when faced with a proprietary brand-name-or-equal specification. In such cases, the designer requires the contractor to use a specified material brand or any other material that is “equal.” However, the conditions of “equal” are usually not explicit. Five algorithms structured in pseudocode terms are provided for such equality interpretation. The paper concludes that a better understanding of design-process knowledge can strengthen the communication between design and construction. [ABSTRACT FROM AUTHOR]

Presents a paper aimed at identifying improved methods of management by contractors for their in-house workers' compensation insurance programs. Examination of experience modification rate (EMR); Inability of many contractors to take active role in workers' compensation management; Reference to improved workers' compensation management practices; Discussion of EMR as a measure of a contractor's safety performance.

Provides information on the results of a study which surveyed workers' compensation practices in the construction industry. Classification of the contractors; Findings from workers, owners and insurance professionals; Analysis of data collected from the survey.

A key underlying challenge in today's model-driven approach to engineering is how progress and level of effort are being measured and reported. Recent definitions such as level of development enable project teams to track completeness of information in building information modeling (BIM)-driven processes; yet, there are no established processes or metrics to reliably measure progress or track productivity of a model-driven engineering process as a function of the maturity of the model and/or the data that supports the engineering process. To address this gap in knowledge, this paper presents a discrete set of model maturity index (MMI) definitions to measure progress in engineering work, per modeling discipline. Using these MMI definitions, a model maturity risk index (Model MRI) toolkit was created to track maturity of an engineering process at the granularity of work breakdown structure locations. The MMI toolkit also reports risk in achieving certain MMI levels per modeling discipline. To benchmark maturity of the modeling work per project milestone or model review session and to establish workflows for tracking progress, an addendum to the modeling execution plan (ModelXP) is presented. New productivity and project controls metrics for tracking modeling work are also introduced. The developed definitions, toolkit, and addendum to ModelXP were validated through exhaustive surveys and charrette tests with project stakeholders involved in capital projects. The role of the MMI definitions and Model MRI toolkit in providing project controls insight and proactive management opportunities in real-world projects is discussed in detail. [ABSTRACT FROM AUTHOR]

The author discusses the contributions to the periodical "Journal of Construction Engineering and Management" (CEM) and the need to focus on broad and global perspective, with particular focus on innovation in research, practice and academic publication.

The article discusses qualities which the journal looks for in articles submitted for publication, presents comments which are commonly provided by reviewers in feedback, and suggests that authors should clearly state their paper's contributions to the body of knowledge.

This paper uses a network analysis approach to better understand the relationships and interdependencies between essential flashtrack practices to identify the most central and core practices for successful execution of flash-track projects. Building on prior studies, this paper describes a practice-to-practice semantic network analysis, which reveals a rank order of the differing measures of essential flash-track practices. The findings indicate that establishing fully integrated teams, personnel selection, contractually aligning project participants and colocation of the project team are the most central and core flash-track enablers. Using a semantic network analysis suggests that the following three practices, which were ranked as Tier II practices based on delphi and analytic hierarchy process (AHP) approach in the earlier research, are found to be core and enabling practices and thus merit to be promoted to the Tier I flash-track practices: (1) colocation of the project team; (2) staffing with cooperative and collaborative personnel; and (3) seeking out suppliers and specialty contractors as a source for time-saving innovations. In addition, this research indicates that the following three Tier I practices (according to delphi and AHP) even though important are not considered core or enabling practices (according to network analysis): (1) emphasizing coordination planning during the design process; (2) providing enough resources to critical path items; and (3) simplifying approval procedures. This research offers practical benefits by revealing why a particular flash-track practice is deemed essential. Although there has been some use of network analysis, particularly social network analysis in construction industry research, the authors believe that this flash-track practice-to-practice semantic network analysis is unique. [ABSTRACT FROM AUTHOR]

This paper presents the initial findings of an ongoing research effort focused on identifying, quantifying, and ranking essential industry practices for the successful delivery of faster fast-track, or Flash Track, projects. The research data collection involves literature search, engineering, procurement, construction industry interviews, and discussions with an industry expert panel. This research has extensively explored and analyzed U.S. and international practices and has deeply explored project execution practices in industries other than construction, including manufacturing, shipbuilding, and software engineering/development. The methodology employed in this research entails deployment of a three-round Delphi process and an Analytic Hierarchy Process to produce a comprehensive list of prioritized recommendations on essential Flash Track practices. This paper contributes to the body of knowledge by identifying and ranking 18 industry best practices essential to successfully manage the unique challenges and risks inherent in Flash Track projects. This research finds that planning, execution, and organizational practices are the most essential considerations for successful Flash Tracking. It also highlights the central role of trusting relationships and fully integrated work teams and, in doing so, extends earlier studies calling for innovative approaches, rethinking prevailing practices, and entertaining a new paradigm in Flash Track projects. [ABSTRACT FROM AUTHOR]

Efficient data collection of high-quantity and low-cost highway assets such as road signs, traffic signals, light poles, and guardrails is a critical element to the operation, maintenance, and preservation of transportation infrastructure systems. Despite its importance, current practice of highway asset data collection is time-consuming, subjective, and potentially unsafe. The high volume of the data that needs to be collected can also negatively impact the quality of the analysis. To address these limitations, this paper proposes a new algorithm for semantic segmentation and recognition of highway assets using video frames collected from a car-mounted camera. The proposed set of algorithms (1) takes the captured frames and using a pipeline of structure from motion and multiview stereo reconstructs a three-dimensional (3D) point cloud model of the highway and surrounding assets; (2) using a Semantic Texton Forest classifier, each geo-registered two-dimensional (2D) video frame at the pixel-level is segmented based on shape, texture, and color of the highway assets; and finally, (3) based on the results of the 2D segmentation and a new voting scheme, each reconstructed 3D point in the cloud is also categorized for one type of asset and is color coded accordingly. The resulting augmented reality environment that integrates the color-coded point clouds with the geo-registered video frames enables a user to conduct visual walk through and query different categories of assets. Experiments were performed on a challenging video data set containing sequences filmed from a moving car on a 2.2-mi-long, two-lane highway research facility. Experimental results with an average accuracy of 76.50 and 86.75% in segmentation and pixel-level recognition of 12 types of asset categories reflect the promise of the applicability of this approach for segmentation and recognition of highway assets from image-based 3D point clouds. It also enables future algorithmic developments for 3D localization of traffic signs and other assets that are detected using the state-of-the-art vision-based methods. [ABSTRACT FROM AUTHOR]