Deep learning-based automated productivity monitoring for on-site module installation in off-site construction

Baek, Jongyeon; Kim, Daeho; Choi, Byungjoo

doi:10.1016/j.dibe.2024.100382

Cited by 4 publications

(1 citation statement)

References 70 publications

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“…Building information modeling (BIM) and geographic information systems (GISs) can be used to collect detailed 3D spatial data [58], while large-scale distributed devices and IoT sensors can be used to gather industrial processing data [57,59]. Additionally, machine vision and deep learning techniques can be utilized to collect and analyze on-site data points, images, audios, and videos, enhancing monitoring and security measures [60,61]. Techniques such as wireless sensor systems can help in gathering environmental parameters, and artificial speech recognition technology can capture voice commands in smart home environments, integrating these into broader security protocols [62].…”

Section: Data Sourcesmentioning

confidence: 99%

Cyber Risk Assessment Framework for the Construction Industry Using Machine Learning Techniques

Yao,

García de Soto

2024

Buildings

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Construction 4.0 integrates digital technologies that increase vulnerability to cyber threats. A dedicated cyber risk assessment framework is essential for proactive risk mitigation. However, existing studies on this subject within the construction sector are scarce, with most discussions still in the preliminary stages. This study introduces a cyber risk assessment framework that integrates machine learning techniques, pioneering a data-driven approach to quantitatively assess cyber risks while considering industry-specific vulnerabilities. The framework builds on over 20 literature reviews related to construction cybersecurity and semi-structured interviews with two industry experts, ensuring both rigor and alignment with practical industrial needs. This study also addresses the challenges of data collection and proposes potential solutions, such as a standardized data collection format with preset fields that computers can automatically populate using data from construction companies. Additionally, the framework proposes dynamic machine learning models that adjust based on new data, facilitating continuous risk monitoring tailored to industry needs. Furthermore, this study explores the potential of advanced language models in cybersecurity management, positioning them as intelligent cybersecurity consultants that provide answers to security inquiries. Overall, this study develops a conceptual machine learning framework aimed at creating a robust, off-the-shelf cyber risk management system for industry practitioners.

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Section: Data Sourcesmentioning

confidence: 99%

Cyber Risk Assessment Framework for the Construction Industry Using Machine Learning Techniques

Yao,

García de Soto

2024

Buildings

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Intelligent construction benefits the public: evidence from the opinion analysis on social media

Wang,

Yuan,

Chen

et al. 2024

ECAM

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PurposeThe construction and real estate sectors are vital to national economies, but traditional construction methods often lead to challenges such as safety risks, noise and environmental pollution. While intelligent construction is believed to mitigate these issues, there is a lack of solid empirical evidence on whether it truly benefits the general public. This paper seeks to explore the societal benefits of intelligent construction from the public’s perspective, addressing this research gap.Design/methodology/approachThe research adopts a two-step approach. First, topic mining is conducted to identify topics closely related to the public’s daily life, such as environmental impact, construction traffic management and construction technologies. These topics are then analyzed through sentiment analysis using a bidirectional long short-term memory model with attention mechanism to determine whether the public has a favorable view of these aspects of intelligent construction, indirectly demonstrating the benefits to the public.FindingsThe primary topics identified include “industry development,” “technology enterprise,” “construction equipment,” “intelligent technology,” “environmental protection,” “robots” and “construction traffic management.” Sentiment analysis shows that public sentiment is overwhelmingly positive across all topics and regions, with “environmental protection,” “construction traffic management” and “robots” receiving the most favorable reactions.Originality/valueThis study provides empirical evidence of the societal benefits of intelligent construction from the public’s viewpoint using social media data. The results highlight the need for continued promotion and adoption of intelligent construction due to its positive impact on society.

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Construction dynamic monitoring and management in whole-process engineering consulting

Fang

2024

2024 International Conference on Big Data and Digital Management

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Deep learning-based automated productivity monitoring for on-site module installation in off-site construction

Cited by 4 publications

References 70 publications

Cyber Risk Assessment Framework for the Construction Industry Using Machine Learning Techniques

Cyber Risk Assessment Framework for the Construction Industry Using Machine Learning Techniques

Intelligent construction benefits the public: evidence from the opinion analysis on social media

Construction dynamic monitoring and management in whole-process engineering consulting

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