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1. Invariant Signature, Logic Reasoning, and Semantic Natural Language Processing (NLP)-Based Automated Building Code Compliance Checking (I-SNACC) Framework

   https://doi.org/10.36680/j.itcon.2023.001  

   Wu, Jin ; Xue, Xiaorui ; Zhang, Jiansong ( January 2023 , Journal of Information Technology in Construction)

   Traditional manual building code compliance checking is costly, time-consuming, and human error-prone. With the adoption of Building Information Modeling (BIM), automation in such a checking process becomes more feasible. However, existing methods still face limited automation when applied to different building codes. To address that, in this paper, the authors proposed a new framework that requires minimal input from users and strives for full automation, namely, the Invariant signature, logic reasoning, and Semantic Natural language processing (NLP)-based Automated building Code compliance Checking (I-SNACC) framework. The authors developed an automated building code compliance checking (ACC) prototype system under this framework and tested it on Chapter 10 of the International Building Codes 2015 (IBC 2015). The system was tested on two real projects and achieved 95.2% precision and 100% recall in non-compliance detection. The experiment showed that the framework is promising in building code compliance checking. Compared to the state-of-the-art methods, the new framework increases the degree of automation and saves manual efforts for finding non-compliance cases. 

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2. A machine learning-based approach for building code requirement hierarchy extraction

   Zhang, Ruichuan ; El-Gohary, Nora ( January 2019 , 2019 CSCE Annual Conference)

   Most of the existing automated code compliance checking (ACC) methods are unable to fully automatically convert complex building-code requirements into computer-processable forms. Such complex requirements usually have hierarchically complex clause and sentence structures. There is, thus, a need to decompose such complex requirements into hierarchies of much smaller, manageable requirement units that would be processable using most of the existing ACC methods. Rule-based methods have been used to deal with such complex requirements and have achieved high performance. However, they lack scalability, because the rules are developed manually and need to be updated and/or adapted when applied to a different type of building code. More research is, thus, needed to develop a scalable method to automatically convert the complex requirements into hierarchies of requirement units to facilitate the succeeding steps of ACC such as information extraction and compliance reasoning. To address this need, this paper proposes a new, machine learning-based method to automatically extract requirement hierarchies from building codes. The proposed method consists of five main steps: (1) data preparation and preprocessing; (2) data adaptation; (3) deep neural network model training for dependency parsing; (4) automated requirement segmentation and restriction interpretation based on the extracted dependencies; and (5) evaluation. The proposed method was trained using the English Treebank data; and was tested on sentences from the 2009 International Building Code (IBC) and the Champaign 2015 IBC Amendments. The preliminary results show that the proposed method achieved an average normalized edit distance of 0.32, a precision of 89%, a recall of 76%, and an F1-measure of 82%, which indicates good requirement hierarchy extraction performance. 

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3. Evaluation of Seven Part-of-Speech Taggers in Tagging Building Codes: Identifying the Best Performing Tagger and Common Sources of Errors

   Xue, X ; Zhang, J. ( January 2020 , ASCE Construction Research Congress 2020)

   As the number, size and complexity of building construction projects increase, code compliance checking becomes more challenging because of the time-consuming, costly, and error-prone nature of a manual checking process. A fully automated code compliance checking would be desirable in facilitating a more efficient, cost effective, and human error-proof code checking. Such automation requires automated information extraction from building designs and building codes, and automated information transformation to a format that allows automated reasoning. Natural Language Processing (NLP) is an important technology to support such automated processing of building codes, because building codes are represented in natural language texts. Part-of-speech (POS) tagging, as an important basis of NLP tasks, must have a high performance to ensure the quality of the automated processing of building codes in such a compliance checking system. However, no systematic testing of existing POS taggers on domain specific building codes data have been performed. To address this gap, the authors analyzed the performance of seven state-of-the-at POS taggers on tagging building codes and compared their results to a manually-labeled gold standard. The authors aim to: (1) find the best performing tagger in terms of accuracy, and (2) identify common sources of errors. In providing the POS tags, the authors used the Penn Treebank tagset, which is a widely used tagset with a proper balance between conciseness and information richness. An average accuracy of 88.80% was found on the testing data. The Standford coreNLP tagger outperformed the other taggers in the experiment. Common sources of errors were identified to be: (1) word ambiguity, (2) rare words, and (3) unique meaning of common English words in the construction context. The found result of machine taggers on building codes calls for performance improvement, such as error-fixing transformational rules and machine taggers that are trained on building codes. 

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4. Semantic Representation Learning and Information Integration of BIM and Regulations

   https://doi.org/10.1061/9780784483893.058  

   Zhang, Ruichuan ; El-Gohary, Nora ( May 2022 , Computing in Civil Engineering 2021)

   Issa, R. (Ed.)

   Automated checking of the compliance of building information modeling (BIM)-based building designs with relevant codes and regulations requires bridging the semantic gap between the Industry Foundation Classes (IFC) schema and the natural language. In most of the existing automated compliance checking (ACC) systems, the integration of the IFC schema and natural language is realized through hardcoding or predefined rules, ontologies, or dictionaries. These methods require intensive manual engineering effort and are often rigid and difficult to generalize. There is, thus, a need for an automated and meanwhile flexible and generalizable information integration method. To address this need, this paper leverages transformer-based language models to learn the semantic representations of concepts in the building information models (BIMs) and regulatory documents. An automated IFC-regulatory information integration approach based on these learned semantic representations is proposed. The preliminary experimental results show that the proposed approach achieved promising performance—an accuracy of 80%—on integrating IFC and regulatory concepts. 

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5. A Machine-Learning Approach for Semantically-Enriched Building-Code Sentence Generation for Automatic Semantic Analysis

   https://doi.org/10.1061/9780784482865.133  

   Zhang, Ruichuan ; El-Gohary, Nora ( November 2020 , Construction Research Congress 2020)

   Tang, P. ; Grau, D. ; El Asmar, M. (Ed.)

   Existing automated code checking (ACC) systems require the extraction of requirements from regulatory textual documents into computer-processable rule representations. The information extraction processes in those ACC systems are based on either human interpretation, manual annotation, or predefined automated information extraction rules. Despite the high performance they showed, rule-based information extraction approaches, by nature, lack sufficient scalability—the rules typically need some level of adaptation if the characteristics of the text change. Machine learning-based methods, instead of relying on hand-crafted rules, automatically capture the underlying patterns of the existing training text and have a great capability of generalizing to a variety of texts. A more scalable, machine learning-based approach is thus needed to achieve a more robust performance across different types of codes/documents for automatically generating semantically-enriched building-code sentences for the purpose of ACC. To address this need, this paper proposes a machine learning-based approach for generating semantically-enriched building-code sentences, which are annotated syntactically and semantically, for supporting IE. For improved robustness and scalability, the proposed approach uses transfer learning strategies to train deep neural network models on both general-domain and domain-specific data. The proposed approach consists of four steps: (1) data preparation and preprocessing; (2) development of a base deep neural network model for generating semantically-enriched building-code sentences; (3) model training using transfer learning strategies; and (4) model evaluation. The proposed approach was evaluated on a corpus of sentences from the 2009 International Building Code (IBC) and the Champaign 2015 IBC Amendments. The preliminary results show that the proposed approach achieved an optimal precision of 88%, recall of 86%, and F1-measure of 87%, indicating good performance. 

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