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
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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.
Dyson, Karen; Dawwas, Emad; Poulton Kamakura, Renata; Alberti, Marina; Fuentes, Tracy L. (2023). Say Where You Sample: Increasing Site Selection Transparency in Urban Ecology. Ecosphere, 14(3).
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Abstract
Urban ecological studies have the potential to expand our understanding of socioecological systems beyond that of an individual city or region. Cross-comparative empirical work and synthesis are imperative to develop a general urban ecological theory. This can be achieved only if studies are replicable and generalizable. Transparency in methods reporting facilitates generalizability and replicability by documenting the decisions scientists make during the various steps of research design; this is particularly true for sampling design and selection because of their impact on both internal and external validity and the potential to unintentionally introduce bias. Three interdependent aspects of sample design are study sample selection (e.g., specific organisms, soils, or water), sample specification (measurement of specific variable of interest), and site selection (locations sampled). Of these, documentation of site selection—the where component of sample design—is underrepresented in the urban ecology literature. Using a stratified random sample of 158 papers from 12 major urban ecology journals, we investigated how researchers selected study sites in urban ecosystems and evaluated whether their site selection methods were transparent. We extracted data from these papers using a 50-question, theory-based questionnaire and a multiple-reviewer approach. Our sample represented almost 45 years of urban ecology research across 40 different countries. We found that more than 80% of the papers we read were not transparent in their site selection methodology. We do not believe site selection methods are replicable for 70% of the papers read. Key weaknesses include incomplete descriptions of populations and sampling frames, urban gradients, sample selection methods, and property access. Low transparency in reporting the where methodology limits urban ecologists' ability to assess the internal and external validity of studies' findings and to replicate published studies; it also limits the generalizability of existing studies. The challenges of low transparency are particularly relevant in urban ecology, a field where standard protocols for site selection and delineation are still being developed. These limitations interfere with the fields' ability to build theory and inform policy. We conclude by offering a set of recommendations to increase transparency, replicability, and generalizability.
Keywords
external validity, field ecology, generalizability, internal validity, replication, reproducibility, sampling design, site selection, theory building, transparency
Choi, Kunhee, Bae, Junseo, Yin, Yangtian, and Lee, Hyun Woo. (2014). ACT²: Time–Cost Tradeoffs from Alternative Contracting Methods. Journal of Management in Engineering, 37(1).
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Abstract
Incentive/disincentive (I/D) and cost-plus-time (A+B) are two of the most widely used alternative contracting methods (ACMs) for accelerating the construction of highway infrastructure improvement projects. However, little is known about the effects of trade-offs in terms of project schedule and cost performance. This study addresses this problem by creating and testing a stochastic decision support model called accelerated alternative contracting cost-time trade-off (ACT2). This model was developed by a second-order polynomial regression analysis and validated by the predicted error sum of square statistic and paired comparison tests. The results of a descriptive trend analysis based on a rich set of high-confidence project data show that I/D is effective at reducing project duration but results in higher cost compared to pure A+B and conventional methods. This cost-time trade-off effect was confirmed by the ACT2 model, which determines the level of cost-time trade-off for different ACMs. This study will help state transportation agencies promote more effective application of ACMs by providing data-driven performance benchmarking results when evaluating competing acceleration strategies and techniques.
Keywords
Errors (statistics), Project management, Benefit cost ratios, Regression analysis, Construction costs, Infrastructure construction, Contracts and subcontracts, Construction methods
Kim, Minju; Lee, Dongmin; Kim, Taehoon; Oh, Sangmin; Cho, Hunhee. (2023). Automated Extraction of Geometric Primitives with Solid Lines from Unstructured Point Clouds for Creating Digital Buildings Models. Automation In Construction, 145.
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Abstract
Point clouds produced by laser scanners are an invaluable source of data for reconstructing multi-dimensional digital models that reflect the as-is conditions of built facilities. However, previous studies aimed to reconstruct models by overlaying the dataset on top of ground-truth reference models to manually adjust the accuracy of the output. Therefore, this paper describes the extraction of geometric primitives with solid lines—the simplest form of objectified data that computer-aided design systems can handle—from unorganized data points and creation of digital models of built facilities in a form of floor plan. The geometric primitives are extracted from 3D points by hybridizing machine learning algorithms, which are mean-shift clustering, non-convex hull, and random sample and consensus (RANSAC). This paper provides a solution for creating a new form of as-built model with high accuracy and robustness from scratch without the involvement of ground-truth solutions or manual adjustments. © 2022 Elsevier B.V.
Keywords
Computer Aided Design; Geometry; Laser Applications; Learning Algorithms; Machine Learning; Scanning; As-build Model Creation; Build Facility; From-point-to-line; Geometric Primitives; Laserscanners; Model Creation; Outline Extractions; Point-clouds; Point-to-line; Solid Lines
Ruoniu (Vince) Wang is an Assistant Professor in the Runstad Department of Real Estate in the College of Built Environments at the University of Washington. He studies spatial justice and inclusive communities, including their impacts reflected in the built environment, human behaviors, and policy interventions. Vince joined the University of Washington after serving six years as the research manager and director in a national non-profit organization Grounded Solutions Network. He has designed and conducted a U.S. Census of inclusionary housing policies, a U.S. census of community land trusts, and a national performance evaluation of shared equity homeownership programs. His research expands to policy evaluation for the two largest federal assisted housing rental programs in the U.S.: the Low-Income Housing Tax Credit program and the Housing Choice Voucher program. Vince grounds his research with applied tools to democratize data for low-income communities.
Lingzi Wu is an Assistant Professor with the Department of Construction Management (CM) at the University of Washington (UW). Prior to joining UW in September 2022, Dr. Wu served as a postdoctoral fellow in the Department of Civil and Environmental Engineering at University of Alberta, where she received her MSc and PhD in Construction Engineering and Management in 2013 and 2020 respectively. Prior to her PhD, Dr. Wu worked in the industrial construction sector as a project coordinator with PCL Industrial Management from 2013 to 2017.
An interdisciplinary scholar focused on advancing digital transformation in construction, Dr. Wu’s current research interests include (1) integration of advanced data analytics and complex system modeling to enhance construction practices and (2) development of human-in-the-loop decision support systems to improve construction performance (e.g., sustainability and safety). Dr. Wu has published 10 papers in top journals and conference proceedings, including the Journal of Construction Engineering and Management, Journal of Computing in Civil Engineering, and Automation in Construction. Her research and academic excellence has received notable recognition, including a “Best Paper Award” at the 17th International Conference on Modeling and Applied Simulation, and the outstanding reviewer award from the Journal of Construction Engineering and Management.
As an educator and mentor, Dr. Wu aims to create an inclusive, innovative, and interactive learning environment where students develop personal, technical, and transferable skills to grow today, tomorrow, and into the future.
Celina Balderas Guzmán, PhD, is Assistant Professor in the Department of Landscape Architecture. Dr. Balderas’ research spans environmental planning, design, and science and focuses on climate adaptation to sea level rise on the coast and urban stormwater inland. On the coast, her work demonstrates specific ways that the climate adaptation actions of humans and adaptation of ecosystems are interdependent. Her work explores how these interdependencies can be maladaptive by shifting vulnerabilities to other humans or non-humans, or synergistic. Using ecological modeling, she has explored these interdependencies focusing on coastal wetlands as nature-based solutions. Her work informs cross-sectoral adaptation planning at a regional scale.
Inland, Dr. Balderas studies urban stormwater through a social-ecological lens. Using data science and case studies, her work investigates the relationship between stormwater pollution and the social, urban form, and land cover characteristics of watersheds. In past research, she developed new typologies of stormwater wetlands based on lab testing in collaboration with environmental engineers. The designs closely integrated hydraulic performance, ecological potential, and recreational opportunities into one form.
Her research has been funded by major institutions such as the National Science Foundation, National Socio-Environmental Synthesis Center, UC Berkeley, and the MIT Abdul Latif Jameel Water and Food Systems Lab. She has a PhD in the Department of Landscape Architecture and Environmental Planning from the University of California, Berkeley. Previously, she obtained masters degrees in urban planning and urban design, as well as an undergraduate degree in architecture all from MIT.
Shakouri, Mahmoud; Lee, Hyun Woo. (2016). Mean-Variance Portfolio Analysis Data For Optimizing Community-Based Photovoltaic Investment. Data In Brief, 6, 840 – 842.
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Abstract
The amount of electricity generated by Photovoltaic (PV) systems is affected by factors such as shading, building orientation and roof slope. To increase electricity generation and reduce volatility in generation of PV systems, a portfolio of PV systems can be made which takes advantages of the potential synergy among neighboring buildings. This paper contains data supporting the research article entitled: PACPIM: new decision-support model of optimized portfolio analysis for community-based photovoltaic investment [1]. We present a set of data relating to physical properties of 24 houses in Oregon, USA, along with simulated hourly electricity data for the installed PV systems. The developed Matlab code to construct optimized portfolios is also provided in Supplementary materials. The application of these files can be generalized to variety of communities interested in investing on PV systems. (C) 2016 The Authors. Published by Elsevier Inc.
Keywords
Community Solar; Photovoltaic System; Portfolio Theory; Energy Optimization; Electricity Volatility
Zhang, Jinlei; Chen, Feng; Shen, Qing. (2019). Cluster-based LSTM Network for Short-term Passenger Flow Forecasting in Urban Rail Transit. Ieee Access, 7, 147653 – 147671.
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Abstract
Short-term passenger flow forecasting is an essential component for the operation of urban rail transit (URT). Therefore, it is necessary to obtain a higher prediction precision with the development of URT. As artificial intelligence becomes increasingly prevalent, many prediction methods including the long short-term memory network (LSTM) in the deep learning field have been applied in road transportation systems, which can give critical insights for URT. First, we propose a novel two-step K-Means clustering model to capture not only the passenger flow variation trends but also the ridership volume characteristics. Then, a predictability assessment model is developed to recommend a reasonable time granularity interval to aggregate passenger flows. Based on the clustering results and the recommended time granularity interval, the LSTM model, which is called CB-LSTM model, is proposed to conduct short-term passenger flow forecasting. Results show that the prediction based on subway station clusters can not only avoid the complication of developing numerous models for each of the hundreds of stations, but also improve the prediction performance, which make it possible to predict short-term passenger flow on a network scale using limited dataset. The results provide critical insights for subway operators and transportation policymakers.
Keywords
Traffic Flow; Neural-network; Prediction; Ridership; Models; Volume; Lstm; Short-term Passenger Flow Forecasting; Urban Rail Transit; K-means Clustering; Deep Learning
Bogus, Susan M.; Migliaccio, Giovanni C.; Cordova, Arturo A. (2010). Assessment of Data Quality for Evaluations of Manual Pavement Distress. Transportation Research Record, 2170, 1 – 8.
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Abstract
Assessment of the conditions of current assets is a task of major relevance in a transportation agency asset management program It not only provides information on the current condition of the asset but also helps the agency make decisions on future maintenance and rehabilitation activities Although low volume roadways represent a large proportion of the total road network in the United States little research on the management of these assets has been done Two major data collection techniques are used for roadway condition assessment manual and automated Although automated techniques have been found to be safer and quicker manual condition surveys have been proven to offer preciseness and cost effectiveness In the case of low volume roadway assessment for which the funds available to asset managers are limited manual condition surveys are often preferred Nevertheless manual condition surveys must address the potential subjectivity of the results Therefore agencies could benefit from a system for ensuring quality on manual condition surveys This paper proposes a framework for assessment of data quality and presents a case study of its implementation in the Northern New Mexico Pavement Evaluation Program The proposed framework is easily implementable and able to identify potential and actual data collection issues The framework can be used as part of an asset management program and could be particularly beneficial in the case of low volume roads
Keywords
Interrater Reliability; Agreement; Ratings