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ItemUser-perceived effectiveness of unmanned aircraft system (UAS) integration in infrastructure construction environments(Georgia Institute of Technology, 2018-04-10) Kim, SungjinA multi-layered performance analysis (MPA) method was proposed. Information analysis, technology performance, and human performance addressed based on the users' experience and perception measurement method in this study. Field-testing and participatory user field experiment were also conducted. Results can provide a better understanding of UAS integration and information needs to use the UAS in the construction domain. The findings during field-testing and group interviews can identify important factors and demonstrate the effectiveness of UAS integration based on the identified factors. The main challenge of this study is the small number of the data sample. However, industry representatives who have significant work experience participated, and the result of this study based on their experience and perception could have significant effects on the UAS integration in the construction environment. The MPA method contributes to transforming the research paradigm from the technology-centric method to the human-technology combined approach that considers human performance. The main findings can also function as the foundation to develop practical user guidelines and policy for the construction and infrastructure industry.
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ItemA knowledge-based BIM exchange model for constructability assessment of commercial building designs(Georgia Institute of Technology, 2016-10-24) Zolfagharian, SamanehAt the early design stage of construction projects, designers often rely on general rules of thumb to make critical decisions about the geometry, construction systems, and materials used in their designs without fully evaluating the applicable construction requirements and constraints. However, ease of construction, or constructability, is a critical factor that is best examined at the early stage of construction projects when designs are the most amenable to change. Currently, reviewing a design’s constructability requires that designers spend a significant amount of time manually extracting constructability data from building models. Data extraction for constructability presents a challenging task, especially in large and complex projects, in which designers may neglect important data pertinent to, or extract unnecessary data from, their designs. The absence of a quantitative constructability model in the United States and a schema for extracting the necessary data for an automated constructability assessment of building designs motivated this study to develop a building information modeling-based constructability assessment exchange model. Through a comprehensive review of the literature, seventy-nine constructability attributes were first identified, which were then categorized into six groups using factor analysis based on 298 responses received from a questionnaire-based survey of industry professionals. Then using pairwise comparisons between constructability factors and common building systems used in the United States, a constructability assessment model was developed with the knowledge obtained from construction experts. Next, this study created a constructability exchange model (EM) using the United States National Building Information Modeling Standard™ approach to automate the data extraction required for the constructability assessment. The proposed EM identifies a reusable and consistent data set (e.g., geometry, object structures, relations, and properties) required for constructability assessment of building designs. The constructability EM was validated through an experiment based approach to examine if the model would help designers explore the constructability of designs in less time, assess the constructability of designs more accurately, and formalize the method of constructability assessment. We also validated the constructability EM using the IfcDoc application, so software vendors can use the EM to examine if their importers and exporters comply with the terminology and rule sets it defines. Moreover, domain experts can use it to validate their models to ensure they have all the required information for assessing constructability. Using the proposed constructability assessment model, designers can identify the tradeoffs involved in the constructability of various design alternatives and make informed decisions about any proposed changes. The constructability EM provides formal classifications of construction information that, when implemented, automates the repeated and time-consuming task of constructability assessment of designs.
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ItemGeneralizable surrogate models for the improved early-stage exploration of structural design alternatives in building construction(Georgia Institute of Technology, 2016-01-15) Nourbakhsh, MehdiThe optimization of complex structures is extremely time consuming. To obtain their optimization results, researchers often wait for several hours and even days. Then, if they have to make a slight change in their input parameters, they must run their optimization problem again. This iterative process of defining a problem and finding a set of optimized solutions may take several days and sometimes several weeks. Therefore, to reduce optimization time, researchers have developed various approximation-based models that predict the results of time-consuming analysis. These simple analytical models, known as “meta- or surrogate models,” are based on data available from limited analysis runs. These “models of the model” seek to approximate computation-intensive functions within a considerably shorter time than expensive simulation codes that require significant computing power. One of the limitations of metamodels (or interchangeably surrogate models) developed for the structural approximation of trusses and space frames is lack of generalizability. Since such metamodels are exclusively designed for a specific structure, they can predict the performance of only the structures for which they are designed. For instance, if a metamodel is designed for a ten-bar truss, it cannot predict the analysis results of another ten-bar truss with different boundary conditions. In addition, they cannot be re-used if the topology of a structure changes (e.g., from a ten-bar truss to a 12-bar truss). If designers change the topology, they must generate new sample data and re-train their model. Therefore, the predictability of these exclusive models is limited. From a combination of the analysis of data from structures with various geometries, the objective of this study is to create, test, and validate generalizable metamodels that predict the results of finite element analysis. Developing these models requires two main steps: feature generation and model creation. In the first step, involving the use of 11 features for nodes and three for members, the physical representation of four types of domes, slabs, and walls were transformed into numerical values. Then, by randomly varying the cross-sectional area, the stress value of each member was recorded. In the second step, these feature vectors were used to create, test, and verify various metamodels in an examination of four hypotheses. The results of the hypotheses show that with generalizable metamodels, the analysis of data from various structures can be combined and used for predicting the performance of the members of structures or new structures within the same class of geometry. For instance, given the same radius for all domes, a metamodel generated from the analysis of data from a 700-, 980-, and 1,525-member dome can predict the structural performance of the members of these domes or a new dome with 250 members. In addition, the results show that generalizable metamodels are able to more closely predict the results of a finite element analysis than metamodels exclusively created for a specific structure. A case study was selected to examine the application of generalizable metamodels for the early-stage exploration of structural design alternatives in a construction project. The results illustrates that the optimization with generalizable metamodels reduces the time and cost of the project, fostering more efficient planning and more rapid decision-making by architects, contractors, and engineers at the early stage of construction projects.
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ItemExtending Building Information Modeling (BIM) interoperability to geo-spatial domain using semantic web technology(Georgia Institute of Technology, 2014-08-14) Parvaresh Karan, EbrahimAs Building Information Modeling (BIM) applications become more sophisticated and used within other knowledge domains, the limitations of existing data exchange and sharing methods become apparent. The integration of BIM and Geographic Information System (GIS) can offer substantial benefits to manage the planning process during the design and construction stages. Currently, building (and geospatial) data are shared between BIM software tools through a common data format, such as Industry Foundation Classes (IFC). Because of the diversity and complexity of domain knowledge across BIM and GIS systems, however, these syntactic approaches are not capable of overcoming semantic heterogeneity. This study uses semantic web technology to ensure the highest level of interoperability between existing BIM and GIS tools. The proposed approach is composed of three main steps; ontology construction, semantic integration through interoperable data formats and standards, and query of heterogeneous information sources. Because no application ontology is available to encompass all IFC classes with different attributes, we first develop an IFC-compliant ontology describing the hierarchy structure of BIM objects. Then, we can translate the building's elements and GIS data into semantic web standard formats. Once the information has been gathered from different sources and transformed into an appropriate semantic web format, the SPARQL query language is used in the last step to retrieve this information from a dataset. The completeness of the methodology is validated through a case study and two use case examples.