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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. 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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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. Interactive Visual Representation of Inter-Connected Requirements in Building Codes

   https://doi.org/10.1061/9780784483961.105  

   Xue, Xiaorui ; Zhang, Jiansong ; El-Gohary, Nora ( March 2022 , Construction Research Congress 2022)

   To facilitate a better understanding of building codes, the visualization of the embedded structures of the provisions and requirements of the codes is needed. Existing research efforts in building code compliance checking mostly do not purposefully represent building codes in formats that facilitate human understanding and interaction with the codes, such as XML and hypertext (text with links to other text). Visual programming commonly represents building codes more visually as flowcharts. However, flowcharts are static, and the generation of flowcharts is still manual. To address this lack of interactive visual representation of building code requirement structures, this paper proposes an automated building code structure extraction and visualization method for visualizing building code contents in a way that clearly shows the inter-connections between requirements and allows intuitive user interaction. In this method, to extract the chapter-section-subsection hierarchical structure and cross-reference structure, a new extraction method named Building Code Network Generator (BCNG) is proposed to automatically generate an interactive visualization using a directed network. The performance of the proposed BCNG was empirically tested on Chapters 5 and 10 of the International Building Code 2015, with a resulting precision, recall, and F1-score of 99.4%, 96.3%, and 97.8%, respectively. In addition, the extracted hierarchical and cross-reference structures were displayed using an open-source network visualization tool to facilitate human understanding and interactions with the building code requirements in automated compliance checking systems. 

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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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