The construction industry is information-intensive, and building information modeling (BIM) has been proposed as an information source for supporting decision making by construction project team members in the architecture, engineering, construction, and operation (AECO) industry. Because building information models contain more building data, further use of the aggregated building information to support construction and operation activities has become important. In Industry 4.0, similar-to-real-life virtual assistants, e.g., Apple’s Siri and Google Assistant, are becoming ever more popular. This research developed a query-answering (QA) system for BIM information extraction (IE) by using natural language processing (NLP) methods to build a virtual assistant for construction project team members. The architecture of the developed QA system for BIM IE consists of three major modules: natural language understanding, IE, and natural language generation. A Python-based prototype application was developed based on the architecture of the QA system for BIM IE to evaluate functionalities of the developed QA system using several BIM/industry foundation classes (IFC) models. Seven building information models and 127 test queries were utilized to evaluate the accuracy of the developed QA system for BIM IE. The experimental results indicated that the developed QA system for BIM IE achieved an 81.9 accuracy score. The developed NLP-based QA system for BIM is valid to provide relatively accurate answers based on natural language queries. The contributions of this research facilitate the development of virtual assistants in the AECO industry, and the architecture of the developed QA system can be extended to queries in other areas.

Among natural biological flocks/swarms or mass social activities, when the collective behavior of the followers has been dominated by the direction or opinion of one leader group, it seems difficult for later-coming leaders to reverse the orientation of the mass followers, especially when they are in quantitative minority. This paper, however, reports a counter-intuitive phenomenon, i.e. Following the Later-coming Minority, provided that the later-comers obey a favorable distribution pattern that enables them to spread their influence to as many followers as possible within a given time and to be dense enough to govern these local followers they can influence directly from the beginning. We introduce a discriminant index to quantify the whole group's orientation under competing leaderships, with which the eventual orientation of the mass followers can be predicted before launching the real dynamical procedure. From the application point of view, this leadership effectiveness index also helps us to design an economical way for the minority later-coming leaders to defeat the dominating majority leaders solely by optimizing their spatial distribution pattern provided that the premeditated goal is available. Our investigation provides insights into effective leadership in biological systems with meaningful implications for social and industrial applications.

The construction industry is generally regarded as information intensive. Building information modeling (BIM) has been developed to provide considerable information support for construction activities. As more data is aggregated in building information models, it is more difficult and time-consuming to extract useful building information for construction project team members with limited BIM experience. In Industry 4.0, deep learning-based conversational artificial intelligence (AI) technologies provide more opportunities to support human daily life. Similarly, for smart construction, a conversational AI information retrieval system is considered important to provide building information support. This research aims to develop a conversational intelligent query answering (QA) system for attribute information retrieval from building information models. The methodology of the QA system for BIM was developed based on the Google pre-trained deep learning language neural network (NN) algorithm—Bidirectional Encoder Representations from Transformers (BERT) and natural language generation (NLG) methods. A Python-based prototype program was developed based on the methodology. Building attribute information-related queries were generated to test the accuracy of the developed QA system for BIM. The preliminary results indicate that the developed prototype achieved an 80.0 accuracy score, meaning 80 predictions exactly matched their ground truths, for providing answers to building attribute information-related queries. The developed QA system methodology for BIM was also found to be valid and relatively accurate. The contribution of this research facilitates the development of conversational AI systems that provide convenient information support for onsite and offsite construction personnel, and the developed QA system for BIM can be extended to other information retrieval areas.