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Harsh Weather Conditions (HWC) are environmental hazards that directly affect construction worker’s health and safety, with a corresponding negative impact on their productivity. Climate predictions predict worsening scenarios of hot and humid weather conditions extending into regions not previously subject to HWC. The predictions pose a significant potential risk and elements of current practices in the construction industry seriously compromise workers safety and productivity. HWC have been shown to have impacts on individuals and influence working environments which taken together lead to inhibiting the achievement of practical, safe and productive practices. This research aims to highlight the need to automatically identify the construction activity intensity in HWC, with proposing a conceptual framework of computerized vision analysis (CVA) system. This framework includes construction activity matrices that classify steel, formwork, and concrete work into macro and micro movements, with including the engaged materials, tools, and equipment. This system is expected to help in measuring the impacts of HWC on construction workers’ safety and its implications on their productivity by identifying the activity intensity level and worker’s idle time at the crew levels.

This paper addresses the application of physiological status monitoring (PSM) for assessing the impact of harsh weather conditions on sports activities in universities in Saudi Arabia. Real sports measurement was conducted during sports activities such that the physiological status (HR and BR) of five students were continuously monitored by using Zephyr BioHarnessTM 3.0 sensors in order to identify the physiological bonds and zones. These bonds and zones were employed as indicators of the associated physiological risks of the performed sports activities. Furthermore, a short yes/no questionnaire was applied to collect information on participants' health conditions and opinions of the applied PSM sensors. The results show the absence of a warning system as a protective aid for the hazardous levels of extremely hot and humid weather conditions that may cause dangerous and fatal circumstances. The applied formulas for estimating maximum HR provides accurate estimations for Maximum Heart Rate (HRmax). The physiological results reveal that the performed activities by the participants are considered the highest category (90-100%) in terms of activity intensity. This category is associated with higher HR, BR and physiological risks including losing the ability to control human body behaviors. Therefore, there is a need for immediate intervention actions to reduce the intensity of the performed activities to safer zones. The outcomes of this study assist the safety improvement of sports activities inside universities and athletes performing their sports activities. To the best of our knowledge, this is the first paper to represent a special case of the application of PSM technology for assessing sports activities in universities considering the impacts of harsh weather conditions on students' health and safety.

Machine failures cause adverse impact on operational efficiency of any manufacturing concern. Identification of such critical failures and examining their associations with other process parameters pose a challenge in a traditional manufacturing environment. This research study focuses on the analysis of critical failures and their associated interaction effects which are affecting the production activities. To improve the fault detection process more accurately and efficiently, a conceptual model towards a smart factory data analytics using cyber physical systems (CPS) and Industrial Internet of Things (IIoTs) is proposed. The research methodology is based on a fact-driven statistical approach. Unlike other published work, this study has investigated the statistical relationships among different critical failures (factors) and their associated causes (cause of failures) which occurred due to material deficiency, production organization, and planning. A real business case is presented and the results which cause significant failure are illustrated. In addition, the proposed smart factory model will enable any manufacturing concern to predict critical failures in a production process and provide a real-time process monitoring. The proposed model will enable creating an intelligent predictive failure control system which can be integrated with production devices to create an ambient intelligence environment and thus will provide a solution for a smart manufacturing process of the future.

Machinery infrastructure is usually prone to stoppage due to unexpected breakdowns. This paper analyzes possible failures root causes of manufacturing facilities, including machinery, to improve their performance. The research methodology utilizes a fact-driven statistical approach to investigate the statistical relationships between different affecting factors. These include failure modes, durations of downtime, working shifts, and products. Experimental designs models were employed in this paper, based on the analysis of variance (ANOVA) test and the Kruskal-Wallis test in combination with Tukey’s method for pairwise comparisons. ANOVA analysis identifies different downtime durations influenced by different types of failures. Similarly, the Kruskal-Wallis test results show that downtime durations are affected by different types of failures and not affected by different products and production lines. A case study was done on a dairy plant in Saudi Arabia where ten types of failures were identified. The results showed the top ranked failures to be finished product clearance, setup delay, raw material rejection, mechanical failure, and electrical failure. The findings help the management to develop learned lessons that help to minimize the cost and consequences of having frequent failures.