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When bidding on construction projects, contractors need to understand the specifications properly to manage project risks. However, specifications are mainly analyzed based on human cognitive abilities, which can take considerable time and can lead to errors due to misunderstanding. While efforts have been made to automate this process, that the existing academic efforts to automate the process have limitations. To develop an automated specification reviewing model applicable to various kinds of specifications, the authors propose information extraction frameworks consisting of five categories. In addition, a named entity recognition (NER) model was developed based on bidirectional long short-term memory architecture to extract information from text data automatically. The data set for model development comprised 56 construction specifications, which included a total of 4,659 sentences labeled according to five categories of information. Word2Vec was utilized to aconvert labeled text data to the form of numeric vectors to be input into the NER model. The NER model successfully assigned every word in the testing data to an appropriate category with a satisfactory performance of 0.919 precision and 0.914 recall. These results contribute to the automation of the construction specification review process. Although this research focused on road construction projects, the proposed information extraction framework can be applied to other types of construction projects. [ABSTRACT FROM AUTHOR]

Detecting contractual risk information from construction specifications is crucial to succeeding in construction projects. This paper describes clause classification using the Bidirectional Encoder Representations from Transformers (BERT) method in natural language processing. Seven risk categories are determined from a literature review, including payment, temporal, procedure, safety, role and responsibility, definition, and reference. Using 2807 clauses from 56 construction specifications, the BERT-based clause classification model returns noticeable performances with 0.889 accuracy for validation and a 0.934 F1 score on testing. The model is evaluated by comparing the clause classification performance with other machine learning methods, including the support vector machine and a simple deep neural network, and shows dominant performance on every risk category. Practitioners in the construction industry are the primary beneficiaries of the research as the model will contribute to improving the construction specification review process and risk management during construction projects. • The BERT-based clause classification model achieved 0.934 of F1 score. • The model detected seven risk contractual categories which are vulnerable to disputes. • The model made the contractual risk detection to be efficient, accurate, and scalable. • First attempt to apply a pre-trained NLP model to construction document review [ABSTRACT FROM AUTHOR]

• Citizen alignment is essential to achieve development goals in smart cities. • Top-down announcements of four smart city governments were analyzed. • The method based on web scraping and natural language processing was used. • Management strategies in smart cities could be further understood. • City governments focus on citizen education and participation. Many city governments have implemented promising smart initiatives to make cities more efficient, livable, and ecological. To harness the full potential of smart city initiatives, it is vital for policymakers to align citizens with the project objectives. This study comprehensively characterizes and classifies top-down announcements formulated by city developers into six alignment categories (i.e., smart economy , smart people , smart governance , smart mobility , smart environment , and smart living) using natural language processing. The proposed framework consists of five main processes: (1) web scraping-based extraction of announcements of four smart cities – Boston, Helsinki, Seoul, and Taipei, (2) text data preprocessing, (3) latent Dirichlet allocation-based modeling of strategic topics, (4) quantification of inter-topic similarities using Hellinger distance, and (5) comparison of top-down communication trends with real-world levels of urban performance. Through the comparative analysis of top-down communication trends and actual urban performances, the top-down discourses of smart cities were deconstructed as a reflection of wider political programs developed to enhance citizen alignment and urban performances. Furthermore, inter-topic similarities were also quantified to reflect whether communication strategies are multidisciplinary and city-tailored. In conclusion, the findings of this study can enhance our understanding and provide workable guidance for future smart city development. [ABSTRACT FROM AUTHOR]

Existing attempts to automate construction document analysis are limited in understanding the varied semantic properties of different documents. Due to the semantic conflicts, the construction specification review process is still conducted manually in practice despite the promising performance of the existing approaches. This research aimed to develop an automated system for reviewing construction specifications by analyzing the different semantic properties using natural language processing techniques. The proposed method analyzed varied semantic properties of 56 different specifications from five different countries in terms of vocabulary, sentence structure, and the organizing styles of provisions. First, the authors developed a semantic thesaurus for construction terms including 208 word-replacement rules based on Word2Vec embedding to understand the different vocabularies. Second, the authors developed a named entity recognition model based on bi-directional long short-term memory with a conditional random field layer, which identified the required keywords from given provisions with an averaged F1 score of 0.928. Third, the authors developed a provision-pairing model based on Doc2Vec embedding, which identified the most relevant provisions with an average accuracy of 84.4%. The web-based prototype demonstrated that the proposed system can facilitate the construction specification review process by reducing the time spent, supplementing the reviewer's experience, enhancing accuracy, and achieving consistency. The results contribute to risk management in the construction industry, with practitioners being able to review construction specifications thoroughly in spite of tight schedules and few available experts. [ABSTRACT FROM AUTHOR]

Field engineers should manually analyze the site appropriateness of every provision in a construction specification by comparing the requirements against the national standards. To support the manual review involving multiple documents, the authors proposed a semantic text-pairing method that identified relevant provisions from different specifications considering the textual properties. First, 2527 provisions were prepared from two construction specifications of highway projects undertaken in Qatar and five national standards from Australia, the United Kingdom, and the United States. Second, the Doc2Vec model trained the provisions and learned the textual features based on Paragraph Vector with Distributed Memory. Third, the provision relevance was estimated by normalizing cosine similarities between provision vectors generated by the Doc2Vec model. The experiments returned promising results, with an average matching accuracy of 84.40%. The results contribute to the specification review by automatically identifying the most relevant provisions and making the process objective and robust to human errors. • The provision pairing model achieved 84.4% matching accuracy. • The model learned semantic textual features from differently organized provisions. • The model made specification review quick, objective, and robust to human errors. • First attempt to analyze different properties of multiple construction specifications. [ABSTRACT FROM AUTHOR]

Despite the increasing use of natural language processing (NLP) in the construction domain, no systematic comparison has been conducted between NLP applications in construction and the latest advancements in NLP within the computer science domain. Therefore, this study compares NLP studies in these two domains. Firstly, a bibliometric analysis was performed on 55 publications in state-of-the-art NLP studies, which identified four main research areas in NLP. Secondly, a systematic review of 202 NLP studies in construction was conducted, presenting representative application areas of NLP and their current technical status. The results reveal a decreasing technology gap between NLP in construction and the state-of-the-art. However, the comparison also highlighted gaps in application areas and methodologies, and eight future research opportunities were proposed. This review serves as a foundation for future studies that aim to apply state-of-the-art NLP technologies in the construction domain. • Systematic comparison was conducted between NLP studies in construction and state-of-the-art. • State-of-the-art NLP methods and the major field of applications (i.e., NLP tasks) were reviewed. • PRISMA and bibliometic analysis were used for the review study. • Technology gaps between NLP in construction and state-of-the-art were presented. • Future research opportunities were suggested to fill the identified technology gaps. [ABSTRACT FROM AUTHOR]