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Title:Automated energy compliance checking in construction
Author(s):Zhou, Peng
Director of Research:El-Gohary, Nora
Doctoral Committee Chair(s):El-Gohary, Nora
Doctoral Committee Member(s):El-Rayes, Khaled A.; Girju, Corina Roxana; Liu, Liang; Golparvar-Fard, Mani Y.
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Automated Energy Compliance Checking
Building Information Modeling
Semantic Information Alignment
Natural Language Processing
Ontology
Text Classification
Information Extraction
Machine Learning
Energy Codes
Contract Specifications
Abstract:Automated energy compliance checking aims to automatically check the compliance of a building design – in a building information model (BIM) – with applicable energy requirements. A significant number of efforts in both industry and academia have been undertaken to automate the compliance checking process. Such efforts have achieved various levels of automation, expressivity, representativeness, accuracy, and efficiency. Despite the contributions of these efforts, there are two main gaps in existing automated compliance checking (ACC) efforts. First, existing methods are not fully-automated and/or not generalizable across different types of documents. They require different degrees of manual efforts to extract requirements from text into computer-processable representations, and matching the concept representations of the extracted requirements to those of the BIM. Second, existing methods only focused on code checking. There is still a lack of efforts that address contract specification checking. To address these gaps, this thesis aims to develop a fully-automated ACC method for checking BIM-represented building designs for compliance with energy codes and contract specifications. The research included six primary research tasks: (1) conducting a comprehensive literature review; (2) developing a semantic, domain-specific, machine learning-based text classification method and algorithm for classifying energy regulatory documents (including energy codes) and contract specifications for supporting energy ACC in construction; (3) developing a semantic, natural language processing (NLP)-enabled, rule-based information extraction method and algorithm for automated extraction of energy requirements from energy codes; (4) adapting the information extraction method and algorithm for automated extraction of energy requirements from contract specifications; (5) developing a fully-automated, semantic information alignment method and algorithm for aligning the representations used in the BIMs to the representations used in the energy codes and contract specifications; and (6) implementing the aforementioned methods and algorithms in a fully-automated energy compliance checking prototype, called EnergyACC, and using it in conducting a case study to identify the feasibility and challenges for developing an ACC method that is fully-automated and generalized across different types of regulatory documents. Promising noncompliance detection performance was achieved for both energy code checking (95.7% recall and 85.9% precision) and contract specification checking (100% recall and 86.5% precision).
Issue Date:2018-12-07
Type:Text
URI:http://hdl.handle.net/2142/102907
Rights Information:Copyright 2018 Peng Zhou
Date Available in IDEALS:2019-02-08
Date Deposited:2018-12


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