Research on the correlation of building mechanical and electrical installation material names based on HMM-NF

Rui Ling

doi:10.1117/12.3026422

9 February 2024 Research on the correlation of building mechanical and electrical installation material names based on HMM-NF

Rui Ling

Author Affiliations +

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130730H (2024) https://doi.org/10.1117/12.3026422
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

The continuous development of the construction industry has spurred the growth of mechanical and electrical (M&E) installation engineering, in which material management is a crucial component. The classification of material names in M&E installation has become increasingly prominent, with traditional manual classification not only being labor-intensive and costly but also inefficient. To address this issue, this paper introduces a Hidden Markov Model based on Named Entity Features (HMM-NF) for the study of the correlation between names of materials used in M&E installation. This model applies the Hidden Markov Model (HMM) and named entity recognition technology to classify and recognize the names of construction M&E installation materials and to establish the correlation between them. Experimental results show that the model can accurately identify and match material names, specifications, units, and other data, scoring the matches in a manner that well describes the correctness of the established correlations. Furthermore, this paper compares the HMM-NF model with the traditional HMM and the Maximum Probability Segmentation Model, demonstrating that the HMM-NF model exhibits higher accuracy and efficiency during both training and segmentation, making it a reasonable and effective model. This research provides new insights and methods for material management in construction M&E installation engineering, helping to enhance the efficiency and precision of material management and thereby optimizing the management of the entire M&E installation process. Additionally, this study offers a new application and reference for the use of Hidden Markov Models in the field of construction.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Rui Ling "Research on the correlation of building mechanical and electrical installation material names based on HMM-NF", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130730H (9 February 2024); https://doi.org/10.1117/12.3026422

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KEYWORDS

Associative arrays

Data modeling

Detection and tracking algorithms

Engineering

Probability theory

Process modeling

Computer programming

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