The Internet-of-things (IoT) and blockchain are growing realities of modern society, and both are rapidly transforming civilization, either separately or in combination. However, the leverage of both technologies for structural health monitoring (SHM) to enable transparent information sharing among involved parties and autonomous decision making has not yet been achieved. Therefore, this study combines IoT with blockchain-based smart contracts for SHM of underground structures to define a novel, efficient, scalable, and secure distributed network for enhancing operational safety. In this blockchain-IoT network, the characteristics of locally centralized and globally decentralized distribution have been activated by dividing them into core and edge networks. This division enhances the efficiency and scalability of the system. The proposed system was effective in simulation for autonomous monitoring and control of structures. After proper design, the decentralized blockchain networks may effectively be deployed for transparent and efficient information sharing, smart contracts-based autonomous decision making, and data security in SHM.

Abstract:The purpose of this study is to analyze the results of construction accidents occurred from 2011 to 2015 in Korea. The annual reports from the Ministry of Employment and Labor, Korea (MOEL), and the annual reports from the Statistics Korea were used for the analysis in this study. The gender, age, company size and accident types were chosen as a category to analyze the trend of various occupational accidents. In order to analyze the characteristics of construction accidents, incidence rates (IRs) and mortality rates (MRs) were calculated. Further, T-tests and ANOVA analysis were performed to discover the relationships among IRs, MRs, and chosen categories. Male workers' IRs and MRs were significantly higher than those of female workers. Construction workers over 40 years of age suffered the most from occupational injuries. In terms of company size, as company size increases, both IRs and MRs tended to decrease. Occupational injuries caused by falls were higher than other accident types each year. This paper will be able to provide information on occupational accidents for establishing strategies to reduce the accident rate in construction sectors of Korea.

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.