Matching Real-World Facilities to Building Information Modeling Data Using Natural Language Processing

Qing-sheng, Xie; Zhou, Xiaoping; Wang, Jia; Gao, Xinao; Chen, Xi; Li, Chun

doi:10.1109/access.2019.2937219

Cited by 36 publications

(20 citation statements)

References 25 publications

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“…Furthermore, we also observed that most of the highly cited studies such as [12], [13], [45], [56]- [58], [60] are published in the top two countries (China and United Kingdom). [10], [13], [20], [47], [48], [50], [56]- [59][30], [31], [39], [41] 14…”

Section: ) Geographical Distribution Of the Selected Studiesmentioning

confidence: 99%

Building Information Modelling (BIM) and the Internet-of-Things (IoT): A Systematic Mapping Study

et al. 2020

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Of recent, Building Information Modelling (BIM) has become an influential paradigm for the development of better project delivery practices to improve construction and operational efficiencies. In the last 6 years, a significant number of studies have been published on the integration of BIM in Internet of Things (IoT). This paper aims to examine the general research productivity, demographics, and trends shaping the research domain. Hence, the paper will also help to identify, categorize, and synthesize important studies in the research domain. In doing so, we adopt an evidence-based systematic mapping methodology to ensure the coverage of key studies through a systematic and unbiased selection and evaluation process which results in the final selection of 55 relevant studies. The results of the mapping study show that the research on the integration of BIM in IoT is gaining more attention in last 6 years with stable and consistent publication output. Prominent application domains, validation methods, contribution facets, research types, and simulation tools in the field of study were identified and presented. Five research types were also identified, i.e. solution proposal, experience paper, evaluation research, validation research, and opinion paper, with solution proposals getting more research attention. In general, the overall demographics of the research domain were presented and discussed.

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Section: ) Geographical Distribution Of the Selected Studiesmentioning

confidence: 99%

Building Information Modelling (BIM) and the Internet-of-Things (IoT): A Systematic Mapping Study

et al. 2020

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“…Machine learning (ML) is a field in the domain of artificial intelligence (AI) [ 10 ] that focuses on providing machines (robots) with learning abilities like humans. ML has various eminent applications in the areas of medical treatment [ 11 ], speech recognition [ 12 ], natural language processing (NLP) [ 13 , 14 ], image processing and signal processing [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

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The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations.

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“…To reduce the threshold of using the BIM object database for practitioners and the general public, Wu et al (2019)proposed a retrieval engine for BIM object database, which mainly retrieved and recommended component-level information. Xie et al (2019) proposed a method of matching real-world facilities to BIM data, which is also on component-level. The BIM model contains information of the whole life cycle of the building.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale Information Retrieval for BIM using Hierarchical Structure Modelling and Natural Language Processing

Wang¹,

Gao²,

Zhou³

et al. 2021

ITcon

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Building Information Modelling (BIM) captures numerous information the life cycle of buildings. Information retrieval is one of fundamental tasks for BIM decision support systems. Currently, most of the BIM retrieval systems focused on querying existing BIM models from a BIM database, seldom studies explore the multi-scale information retrieval from a BIM model. This study proposes a multi-scale information retrieval scheme for BIM jointly using the hierarchical structure of BIM and Natural Language Processing (NLP). Firstly, a BIM Hierarchy Tree (BIH-Tree) model is constructed to interpret the hierarchical structure relations among BIM data according to Industry Foundation Class (IFC) specification. Secondly, technologies of NLP and International Framework for Dictionaries (IFD) are employed to parse and unify the queries. Thirdly, a novel information retrieval scheme is developed to find the multi-scale information associated with the unified queries. Finally, the retrieval method proposed in this study is applied to an engineering case, and the practical results show that the proposed method is effective.

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Matching Real-World Facilities to Building Information Modeling Data Using Natural Language Processing

Cited by 36 publications

References 25 publications

Building Information Modelling (BIM) and the Internet-of-Things (IoT): A Systematic Mapping Study

Building Information Modelling (BIM) and the Internet-of-Things (IoT): A Systematic Mapping Study

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Multi-scale Information Retrieval for BIM using Hierarchical Structure Modelling and Natural Language Processing

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