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Hong Kong construction safety has witnessed substantial improvement in the last three decades, however, accidents still occur frequently as more than 4,000 accidents are reported in the year 2017. Against this background, this research, firstly, aims to investigate the effectiveness of safety training for construction personnel in Hong Kong. A questionnaire is designed to explore the efficacy and weaknesses of mandatory basic safety training. The results indicate the inadequate knowledge of the concept of personal protective equipment as the main weakness of the workers. Secondly, to overcome the training weakness, an Internet-of-Things (IoT) based innovative safety model is designed to provide real-time monitoring of construction site personnel and environment. The proposed model not only identifies real-time personnel safety problems, i.e., near misses, to reduce the accident rates but also stores the digital data to improve future training and system itself. The proposed model in this research provides a cost-effective solution for optimal construction safety to the stakeholders. A cost comparison analysis suggests that the IoT system can provide 1) 78% cost-savings with respect to the traditional manual system and 2) 65% cost-savings with respect to the traditional sensor system.
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Stored only when both papers are in the corpus — 145 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.