Dr. Carrie Sturts Dossick, Associate Dean for Research, and Professor in the department of Construction Management has been featured on the Building Innovation: The Podcast. The podcast episode is Season 2, Episode 1, and is part one of the NBIMS-US™ Series, and discusses the new module for Project BIM Requirements. Listen to the Podcast here: https://www.nibs.org/building-innovation-podcast
Research Theme: Modeling & Scenario Planning
Methodologies of long-term, strategic thinking and modeling on urban potentials; at the scale of materials to regions
Applications of blockchain for construction project procurement
Kim, M., & Kim, Y.-W. (2024). Applications of blockchain for construction project procurement. Automation in Construction, 165, 105550-. https://doi.org/10.1016/j.autcon.2024.105550
Abstract
Blockchain technology has shown potential in enhancing project performance by instilling trust in data sharing among stakeholders, thereby encouraging the stakeholders to ensure a strategic acquisition and resource management through procurement activities. However, despite the recent research efforts on blockchain in the construction sector, there is a lack of knowledge of the status quo in that barely any research investigated the synergy of blockchain and procurement by recognizing the inextricable linkage between procurement management and project delivery system. This paper conducts a systematic review of 54 articles to assess blockchain's potential in addressing issues inherent in the current organizational structures and collaborative efforts. Findings offer profound insight into the current landscape of procurement-specific blockchain research, highlighting areas needing attention. This paper identified opportunities in construction procurement by investigating the extent to which the technology is integrated into the current project management context emphasizing integration and collaboration.
Keywords
Blockchain; Procurement; Construction industry; Procurement process; Project delivery system; Literature review
Machine Learning–Based Bayesian Framework for Interval Estimate of Unsafe-Event Prediction in Construction
Wu, L., Mohamed, E., Jafari, P., & AbouRizk, S. (2023). Machine Learning–Based Bayesian Framework for Interval Estimate of Unsafe-Event Prediction in Construction. Journal of Construction Engineering and Management, 149(11). https://doi.org/10.1061/JCEMD4.COENG-13549
Abstract
Construction safety is a critical concern for industry and academia, and numerous models and algorithms have been developed to predict incidents or accidents to facilitate proactive decision-making. However, previous studies have been limited due to the inability to account for uncertainties because predictions are given as a single value (i.e., Yes or No) and the failure to integrate subjective judgment. To address these limitations, this research proposes a machine learning–based Bayesian framework for predicting construction incidents using interval estimates. This framework combines a state-of-the-art machine-learning algorithm with a binary Bayesian inference model to develop an incident predictor that considers a range of project characteristics and conditions. Notably, this framework also is capable of incorporating historical or subjective judgment through prior selection and outputs the unsafe event prediction as an interval of possibilities, thus accounting for various uncertainties. The efficacy of our framework was demonstrated in a real-life case study, showcasing its practical implications for proactive decision-making and risk management in the construction industry and representing a valuable contribution to the field of construction safety.
New Tool Created by CBE Researchers in partnership with Charles Pankow Foundation
CBE researchers worked with the Charles Pankow Foundation to develop a new Building Owner Assessment Tool (BOAT). Team members included CBE Dean Renée Cheng, FAIA; Associate Dean for Research Carrie Sturts Dossick, Ph.D, P.E.; and Laura Osburn, Ph.D. Other team members included Lingzi Wu, Ph.D. Daniel Dimitrov, and Xianxiang Sean Zhao. The tool was developed in partnership with the American Institute of Architects and the Integrated Project Delivery Alliance. The Building Owner Assessment Tool (BOAT): Helping You Understand Your Culture and Its…
Anna Malesis
Research Interests: urban eco-evolutionary dynamics, urban complexity, landscape ecology, scenario planning, and nature’s contributions to people/nature-based solutions
Statistical Analysis and Representation Models of Working-Days Liquidated Damages
Abdel Aziz, A. M. (2023). Statistical Analysis and Representation Models of Working-Days Liquidated Damages. Journal of Construction Engineering and Management, 149(7). https://doi.org/10.1061/JCEMD4.COENG-13330
Abstract
Contractors tend to challenge the enforceability of liquidated damages (LDs), claiming they are unreasonable, excessive, penalty statements, or concurrently caused. States customarily assert that the LD rates are a genuine reflection of the expenses expected to be suffered when a project gets delayed due to noncompletion. While there are common practices among the states for articulating LD specifications, which generally follow the Federal Code of Regulations, there are no published studies that assist states in comparing their LD rates to those of other states so that the LD rates might be defended. Further, there are no studies that offer models that would uncover the relationship between the LD rates and the contract sizes so that the LD rates might be justified. This work addresses such gaps in the body of knowledge (BOK) in LDs. With emphasis on the working-days (WD) LD rate schedules, the objectives of this work are to characterize the LD rate schedules across the states and to model a formula(s) that would represent the relationship between the WD LD rates and the contract amounts. The data set for the work represents the LD schedules in the standard specifications of all departments of transportation in the United States. Descriptive and cluster statistical analyses were used for the LD rate characterization. For model development, several linear and nonlinear regression models were employed. The results highlighted considerable LDs variability in the smaller contract sizes and exceptional LD rates stability in the larger sizes. Despite the economic differences among the states, it is found that the LD rate is, on average, 0.02 ¢/$ for projects $20 million or above. Below that, the rate increases between 0.03 ¢/$ and 0.18 ¢/$ until the contracts reach $750,000. LD rates tend to decrease sharply with the increase in contract sizes, forming an L or reverse J shape. This pattern proved complex, and only nonlinear regression with transformed variables successfully modeled it. Credible models were obtained after satisfying the least-squares regression assumptions. The work contributes to the BOK by adding a new statistical dimension to understanding LDs and developing regression model(s) that explain the relationships between the LD rates and the contract sizes. The work should help SHAs create, evaluate, and justify their LD rates.
Helping Rural Counties to Enhance Flooding and Coastal Disaster Resilience and Adaptation
In the United States, flooding is a leading cause of natural disasters, with congressional budget office estimates of $54 billion in loss each year. Although both urban and rural areas are highly vulnerable to flood hazards, most natural disaster resilience studies have focused primarily on urban areas, overlooking rural communities. One such area that has been overlooked are the numerous rural communities bordering the Great Lakes. These communities face unprecedented challenges due to rising water levels, particularly since 2012, which have resulted in increased coastal flood hazard. Despite their flooding risk, they continue to lack flood hazard assessments and inundation maps, exacerbating their vulnerability. The Federal Emergency Management Agency (FEMA) commonly recommend counties to use a freely available tool—called HAZUS to develop hazard mitigation plans and enhance community resilience and adaptation. However, the usage of HAZUS for rural communities is challenging due to existing data gaps that limit the analytical potential of HAZUS in these communities. Continued use of standard datasets for HAZUS analysis by rural counties could likely leave the communities underprepared for future flood events. The proposed project’s vision is to develop methods that use remote sensing data resources and citizen engagement (crowdsourcing) to address current data gaps for improved flood hazard modeling and visualization that is scalable and transferable to rural communities.
The results of the project will expand the traditional frontiers of preparedness and resilience to natural disasters by drawing on the expertise and backgrounds of investigators working at the interface of geological engineering, civil engineering, computer science, marine engineering, urban planning, social science, and remote sensing. Specifically, the proposed research will promote intellectual discovery by i) improving our understanding of remote sensing data sources and open-source processing methods to assist rural communities in addressing the data gaps in flood hazard modeling, ii) developing sustainable geospatial visualization tools for communicating hazards to communities, iii) advancing our understanding of the utility of combining remote sensing and crowdsourcing to flood hazard delineation, iv) understanding ways to incentives the crowd for greater participation and accuracy in hazard in addressing natural disasters, and v) identifying critical community resilience indicators through crowdsourcing. These advancements will lead to prepared and resilient rural communities that can effectively mitigate hazards related to lake level rise and flooding.
Assessing the Expectations Gap – Impact on Critical Infrastructure Service Providers’ and Consumers’ Preparedness, and Response
While community lifeline service providers and local emergency managers must maintain coordinated response and recovery plans, their timelines may not match expectations of local consumers of lifeline services. Indeed, it is quite likely consumers have unrealistic expectations about lifeline restoration, which could explain current inadequate levels of disaster preparedness. This hypothesized expectation gap has received little attention because engineering research typically addresses providers’ capacities, whereas disaster research addresses household and business preparedness. Our project will address this neglected issue by assessing consumers’ (households, business owners/managers, nonprofit managers) expectations about lifeline system performance, and comparing them to lifeline provider capacity in a post-hazard event scenario (following a Cascadia subduction zone earthquake of 9.0 magnitude or greater) in two communities—Kirkland and Shoreline, WA (likely to experience most shaking in this scenario).
Our research will assess the role of the expectations gap in influencing consumers’ and providers’ preparedness as well as response. First, we estimate the gap between consumers and providers expectations using an earthquake scenario in two case study communities. We posit that low consumer preparedness for lifeline disruption is in part a function of low expectations that lengthy disruption will occur. Next, we test the effect of providing consumers and providers with information about this gap. Our proposed sharing estimates of lifeline restoration times should change these beliefs if our assumption about this specific basis for low preparedness is correct and if our audiences attend to, process, and act upon this information. In our longitudinal research, consumers (households, businesses, and nonprofits) and lifeline providers will complete two questionnaires each. Besides lifeline provider surveys, we will collect information about lifeline providers’ capabilities and work with them to estimate restoration times using an expert elicitation-based estimation framework. We will address the following research questions:
- What do consumers think is the likely level of critical lifeline disruption from an earthquake and the timeline for restoration?
- What are consumers’ current levels of preparedness for lifeline interruption?
- What do lifeline providers and an independent engineering expert think are providers’ capabilities to maintain and restore lifeline services?
- How do consumers’ expectations compare with providers’ capabilities (expectations gap)?
- How will this study’s feedback about the expectations gap affect consumers’ and providers’ lifeline resilience expectations, as well as their mitigation and preparedness intentions?
A Water Quality Prediction Model for Large-scale Rivers Based on Projection Pursuit Regression in the Yangtze River
Yi, Ze-ji; Yang, Xiao-hua; Li, Yu-qi. (2022). A Water Quality Prediction Model for Large-scale Rivers Based on Projection Pursuit Regression in the Yangtze River. Thermal Science, 26(3), 2561-2567.
Abstract
In recent decades, the Yangtze River Basin, which carries hundreds of millions of people and a substantial economic scale, has been plagued by water quality dete-rioration, threatening considerably sustainable development. In this paper, a sample set is established based on the water quality indexes of chemical oxygen demand and dissolved oxygen obtained by week-by-week monitoring on the main stream of the Yangtze River in Panzhihua, Yueyang, Jiujiang, and Nanjing from 2006 to 2018. The twelve characteristic variables are selected by random forest technique, and the week-by-week dynamic prediction models of chemical oxygen demand and dissolved oxygen at each section of main stream are established by the projection pursuit regression, which can effectively predict the water quality dynamics of the Yangtze River main stream.
Keywords
Pollution; Water Quality; Dynamic Prediction Model; Random Forest; Projection Pursuit Regression; Yangtze River
Dynamic Production Scheduling Model Under Due Date Uncertainty in Precast Concrete Construction
Kim, Taehoon; Kim, Yong-Woo; Cho, Hunhee. (2020). Dynamic Production Scheduling Model Under Due Date Uncertainty in Precast Concrete Construction. Journal Of Cleaner Production, 257.
Abstract
Precast concrete structures (PCs) are widely used in the construction industry to reduce project delivery times and improve quality. On-time delivery of PCs is critical for successful project completion because the processes involving precast concrete are the critical paths in most cases. However, existing models for scheduling PC production are not adequate for use in dynamic environments where construction projects have uncertain construction schedules because of various reasons such as poor labor productivity, inadequate equipment, and poor weather. This research proposes a dynamic model for PC production scheduling by adopting a discrete-time simulation method to respond to due date changes in real time and by using a new dispatching rule that considers the uncertainty of the due dates to minimize tardiness. The model is validated by simulation experiments based on various scenarios with different levels of tightness and due date uncertainty. The results of this research will contribute to construction project productivity with a reliable and economic precast concrete supply chain. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords
Multiple Production; Demand Variability; Supply Chain; Shop; Management; Minimize; Lines; Precast Concrete Production; Dynamic Simulation; Uncertainty; Production Scheduling; Dispatching Rule