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.
Estiri, Hossein; Krause, Andy; Heris, Mehdi P. (2015). Phasic Metropolitan Settlers: A Phase-Based Model for the Distribution of Households in US Metropolitan Regions. Urban Geography, 36(5), 777 – 794.
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Abstract
In this article, we develop a model for explaining spatial patterns in the distribution of households across metropolitan regions in the United States. First, we use housing consumption and residential mobility theories to construct a hypothetical probability distribution function for the consumption of housing services across three phases of household life span. We then hypothesize a second probability distribution function for the offering of housing services based on the distance from city center(s) at the metropolitan scale. Intersecting the two hypothetical probability functions, we develop a phase-based model for the distribution of households in US metropolitan regions. We argue that phase one households (young adults) are more likely to reside in central city locations, whereas phase two and three households are more likely to select suburban locations, due to their respective housing consumption behaviors. We provide empirical validation of our theoretical model with the data from the 2010 US Census for 35 large metropolitan regions.
Keywords
Residential-mobility; Life-course; Housing Consumption; Family; Satisfaction; Migration; Geography; Context; Age; Distribution Patterns; Us Metropolitan Regions; Household
Kang, Mingyu; Moudon, Anne Vernez; Kim, Haena; Boyle, Linda Ng. (2019). Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS. International Journal Of Environmental Research And Public Health, 16(19).
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Abstract
Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modelling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.
Keywords
Traffic Crash; Walking; Collisions; Accidents; Models; Pedestrian Safety; Spatial Autocorrelation; Algorithm
Kang, Bumjoon; Scully, Jason Y.; Stewart, Orion; Hurvitz, Philip M.; Moudon, Anne V. (2015). Split-Match-Aggregate (SMA) Algorithm: Integrating Sidewalk Data with Transportation Network Data in GIS. International Journal Of Geographical Information Science, 29(3), 440 – 453.
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Abstract
Sidewalk geodata are essential to understand walking behavior. However, such geodata are scarce, only available at the local jurisdiction and not at the regional level. If they exist, the data are stored in geometric representational formats without network characteristics such as sidewalk connectivity and completeness. This article presents the Split-Match-Aggregate (SMA) algorithm, which automatically conflates sidewalk information from secondary geometric sidewalk data to existing street network data. The algorithm uses three parameters to determine geometric relationships between sidewalk and street segments: the distance between streets and sidewalk segments; the angle between sidewalk and street segments; and the difference between the lengths of matched sidewalk and street segments. The SMA algorithm was applied in urban King County, WA, to 13 jurisdictions' secondary sidewalk geodata. Parameter values were determined based on agreement rates between results obtained from 72 pre-specified parameter combinations and those of a trained geographic information systems (GIS) analyst using a randomly selected 5% of the 79,928 street segments as a parameter-development sample. The algorithm performed best when the distances between sidewalk and street segments were 12m or less, their angles were 25 degrees or less, and the tolerance was set to 18m, showing an excellent agreement rate of 96.5%. The SMA algorithm was applied to classify sidewalks in the entire study area and it successfully updated sidewalk coverage information on the existing regional-level street network data. The algorithm can be applied for conflating attributes between associated, but geometrically misaligned line data sets in GIS.
Keywords
Geodatabases; Sidewalks; Algorithms; Pedestrians; Digital Mapping; Algorithm; Gis; Pedestrian Network Data; Polyline Conflation; Sidewalk; Built Environment; Physical-activity; Mode Choice; Urban Form; Land-use; Travel; Generation; Walking
Lin, Xiongbin; Maclachlan, Ian; Ren, Ting; Sun, Feiyang. (2019). Quantifying Economic Effects of Transportation Investment Considering Spatiotemporal Heterogeneity in China: A Spatial Panel Data Model Perspective. The Annals Of Regional Science, 63(3), 437 – 459.
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Abstract
Transportation investment plays a significant role in promoting economic development. However, in what scenario and to what extent transportation investment can stimulate economic growth still remains debatable. For developing countries undergoing rapid urbanization, answering these questions is necessary for evaluating proposals and determining investment plans, especially considering the heterogeneity of spatiotemporal conditions. Current literature lacks systematical research to consider the impacts of panel data and spatial correlation issue in examining the economic effects of transportation investment. To fill this gap, this study collects provincial panel data in China from 1997 to 2015 to evaluate multi-level temporal and spatial effects of transportation investment on economic growth by using spatial panel data analysis. Results show that transportation investment leads to significant and positive effects on growth and spatial concentration of economic activities, but these results vary significantly depending on the temporal and spatial characteristics of each province. The economic impacts of transportation investment are quite positive even considering the time lag effects. This study suggests that both central and local governments should carefully evaluate the multifaceted economic effects of transportation investment, such as a balanced transportation investment and economic development between growing and lagging regions, and considering the spatiotemporal heterogeneity of the economic environment.
Keywords
High-speed Rail; Infrastructure Investment; Causal Relationship; Empirical-analysis; Growth; Impact; Productivity; Efficiency; Spillover; Agglomeration; C33; R40; R58; Spatial Analysis; Time Lag; Urbanization; Transportation; Heterogeneity; Economic Growth; Economic Models; Economic Impact; Data Analysis; Spatial Data; Panel Data; Economic Development; Developing Countries--ldcs; Investments; Economic Analysis; Investment; Local Government; China
Shang-hsien Hsieh; Ken-yu Lin; Nai-wen Chi; Hsien-tang Lin. (2015). Domain Knowledge-Based Information Retrieval for Engineering Technical Documents. Ontology In The AEC Industry. A Decade Of Research And Development In Architecture, Engineering And Construction, chapter 1.
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Abstract
Technical documents with complicated structures are often produced in architecture/engineering/construction (AEC) projects and research. Information retrieval (IR) techniques provide a possible solution for managing the ever-growing volume and contexts of the knowledge embedded in these technical documents. However, applying a general-purpose search engine to a domain-specific technical document collection often produces unsatisfactory results. To address this problem, we research the development of a novel IR system based on passage retrieval techniques. The system employs domain knowledge to assist passage partitioning and supports an interactive concept-based expanded IR for technical documents in an engineering field. The engineering domain selected in this case is earthquake engineering, although the technologies developed and employed by the system should be generally applicable to many other engineering domains that use technical documents with similar characteristics. We carry out the research in a three-step process. In the first step, since the final output of this research is an IR system, as a prerequisite, we created a reference collection which includes 111 earthquake engineering technical documents from Taiwan's National Center for Research on Earthquake Engineering. With this collection, the effectiveness of the IR system can be further evaluated onceit is developed. In the second step, the research focuses on creating a base domain ontology using an earthquake-engineering handbook to represent the domain knowledge and to support the target IR system with the knowledge. In step three, the research focuses on the semantic querying and retrieval mechanisms and develops the OntoPassage approach to help with the mechanisms. The OntoPassage approach partitions a document into smaller passages, each with around 300 terms, according to the main concepts in the document. This approach is then used to implement the target domain knowledge-based IR system that allows users to interact with the system and perform concept-based query expansions. The results show that the proposed domain knowledge-based IR system can achieve not only an effective IR but also inform search engine users with a clear knowledge representation.
Keywords
Architecture; Construction; Engineering; Knowledge Based Systems; Ontologies (artificial Intelligence); Query Processing; Search Engines; Knowledge Representation; Concept-based Query Expansions; Base Domain Ontology; Earthquake Engineering; General-purpose Search Engine; Aec Projects; Architecture/engineering/construction Projects; Complicated Structures; Technical Documents; Domain Knowledge-based Information Retrieval