The Impact of Occupational Safety on Logistics and Automation in Industrial Plants

Research on workplace health and safety analyses the integration of work practices with safety, health and wealth of people at work. The aim of occupational safety is to realize a safe and health work environment, eliminating or reducing the risks for workers' safety and health. The objective of this thesis is the study, integration, development and application of innovative approaches and models for decision-making support in the context of occupational safety in industrial plants and logistics. Such methodologies are expected to lead practitioners and decision-makers, in particular safety professionals and companies, in the management of occupational safety. In particular, this research focuses on the integration and application of ergonomics principles to reduce biomechanical overload of manual work, and methodologies and solutions to improve safety of confined space work in industrial plants. The study of biomechanical overload due to manual handling of loads and awkward postures is the object of several researches and publications addressing the ergonomic risk assessment and the ergonomic approach to remove or reduce the risk of manual handling injuries and disorders. Furthermore, when awkward postures are assumed in high-risk workplaces as confined spaces, the overall risk of work is extremely high. Confined space work is a high-risk activity, posing a significant hazard for both workers and rescuers involved in the emergency response. The leading cause of accidents and fatalities in confined spaces is atmospheric condition (Sahli & Armstrong 1992, Harris et al. 2005, Flynn & Susi, 2010, Meeker, Susi & Flynn 2010, Ye 2011, Bellamy 2015). Further common causes are fire, explosion, ignition of flammable contaminants, spontaneous combustion and contact with temperature extremes. Besides, work activities in confined spaces (e.g., welding and maintenance tasks) frequently require awkward and static postures, at high temperatures. This thesis stresses the importance of implementing health and safety interventions at workplace. These interventions have impact not only on enterprise level but also on individual and social levels. Furthermore, protection of human life is a matter of human rights and human life has an invaluable value. In this thesis, the role of occupational safety and safety strategy as means for the improvement of workers and companies’ performances clearly emerges. Two parallel research fields on occupational safety are investigated: ergonomics and confined spaces. Selected data are introduced related to occupational accidents and diseases due to biomechanical overload and work in confined spaces. The literature survey on controls for risk elimination and reduction shows that technology for safety is available. Nevertheless, injuries and accidents still occur, i.e. safety is frequently considered an expensive investment and a compliance obligation. Specifically, administrative and engineering controls for risk elimination and reduction are introduced for each research field. Administrative controls include work procedures and mathematical models for the design of safe work processes. Such control methods reduce the workers' exposure to occupational risk factors. The ergonomic analysis of manual handling activities drives the modelling by multi-objective optimisation problems in the design of administrative controls for the ergonomic risk reduction in different industries. Administrative controls for risks in confined spaces include work procedures, a multi-criteria decision tool and the analysis of the requirements of Internet of Things (IOT) technologies for reducing the risk of confined space work. The introduction of automation to replace manual work and engineering controls for confined space work are analysed for the risk elimination. Results show that the integration of ergonomics and safety principles in the industrial processes plays a leading role in the successful implementation of the overall strategy. Technologies for safe confined space work and technical solutions assisting workers during manual material handling tasks have been the focus of the Solutions Database Project, funded by the Azienda Unità Sanitaria Locale of Bologna (AUSL), Italy. The study of such technological and technical solutions lead to the development of the Solutions Database, a free access database available online for researchers and practitioners (http://safetyengineering.din.unibo.it/en/banca-delle-soluzioni). The thesis ends with the recommendation that companies should integrate workplace health and safety principles to human resource management and work organisation. The management of health and safety issues should be considered to be crucial for workplace development, as a lever to increase performance and productivity. Finally, this research aims to support and reinforce the evolution of the concept of safety in industry, from ex post required obligation, to ex ante optimisation strategy

Measuring and benchmarking the productivity of excavators in infrastructure projects: A deep neural network approach

Inefficiencies in the management of earthmoving equipment greatly contribute to the productivity gap of infrastructure projects. This paper develops and tests a Deep Neural Network (DNN) model for estimating the productivity of excavators and establishing a productivity measure for their benchmark. After investigating current practices for measuring the productivity of earthwork equipment during 13 interviews with selected industry experts, the DNN model was developed and tested in one of the ‘High Speed rail second phase’ (HS2) sites. The accuracy of prediction achieved by the DNN model was evaluated using the coefficient of determination (R2) and the Weighted Absolute Percentage Error (WAPE) resulting in 0.87 and 69.64%, respectively. This is an adequate level of accuracy when compared to other similar studies. However, according to the WAPE method, the accuracy is still 10.36% below the threshold (i.e. 80%) expected by the industry experts. An inspection of the prediction results over the testing period (21 days) revealed better precision in days with high excavation volumes compared to days with low excavation volumes. This was attributed to the likely involvement of manual work (i.e. archaeologists in the case of the selected site) alongside some of the excavators, which caused gaps in telematics data. This indicates that the accuracy attained is adequate, but the proposed approach is more accurate in a highly mechanised environment (i.e. excavation work with equipment predominantly and limited manual interventions) compared to a mixed mechanised-manual working environment. A bottom-up benchmark measure (i.e. excavation rate) that can be used to measure and benchmark the excavation performance of an individual or a group of equipment, through a work area, to a whole site was also proposed and discussed

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

Abstract There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.</jats:p

Utilizing industry 4.0 on the construction site : challenges and opportunities

In recent years a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organisations alike. This paper discusses the current state of the art in the adoption of industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of industry 4.0 technologies. This paper discusses the relevance of the following key industry 4.0 technologies to construction: data analytics and artificial intelligence; robotics and automation; buildings information management; sensors and wearables; digital twin and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This paper also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector; a three-phase use of intelligent assets from the point of manufacture up to after build and a four staged R&D process for the implementation of smart wearables in a digital enhanced construction site

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Intrusion Detection for Cyber-Physical Attacks in Cyber-Manufacturing System

In the vision of Cyber-Manufacturing System (CMS) , the physical components such as products, machines, and tools are connected, identifiable and can communicate via the industrial network and the Internet. This integration of connectivity enables manufacturing systems access to computational resources, such as cloud computing, digital twin, and blockchain. The connected manufacturing systems are expected to be more efficient, sustainable and cost-effective. However, the extensive connectivity also increases the vulnerability of physical components. The attack surface of a connected manufacturing environment is greatly enlarged. Machines, products and tools could be targeted by cyber-physical attacks via the network. Among many emerging security concerns, this research focuses on the intrusion detection of cyber-physical attacks. The Intrusion Detection System (IDS) is used to monitor cyber-attacks in the computer security domain. For cyber-physical attacks, however, there is limited work. Currently, the IDS cannot effectively address cyber-physical attacks in manufacturing system: (i) the IDS takes time to reveal true alarms, sometimes over months; (ii) manufacturing production life-cycle is shorter than the detection period, which can cause physical consequences such as defective products and equipment damage; (iii) the increasing complexity of network will also make the detection period even longer. This gap leaves the cyber-physical attacks in manufacturing to cause issues like over-wearing, breakage, defects or any other changes that the original design didn’t intend. A review on the history of cyber-physical attacks, and available detection methods are presented. The detection methods are reviewed in terms of intrusion detection algorithms, and alert correlation methods. The attacks are further broken down into a taxonomy covering four dimensions with over thirty attack scenarios to comprehensively study and simulate cyber-physical attacks. A new intrusion detection and correlation method was proposed to address the cyber-physical attacks in CMS. The detection method incorporates IDS software in cyber domain and machine learning analysis in physical domain. The correlation relies on a new similarity-based cyber-physical alert correlation method. Four experimental case studies were used to validate the proposed method. Each case study focused on different aspects of correlation method performance. The experiments were conducted on a security-oriented manufacturing testbed established for this research at Syracuse University. The results showed the proposed intrusion detection and alert correlation method can effectively disclose unknown attack, known attack and attack interference that causes false alarms. In case study one, the alarm reduction rate reached 99.1%, with improvement of detection accuracy from 49.6% to 100%. The case studies also proved the proposed method can mitigate false alarms, detect attacks on multiple machines, and attacks from the supply chain. This work contributes to the security domain in cyber-physical manufacturing systems, with the focus on intrusion detection. The dataset collected during the experiments has been shared with the research community. The alert correlation methodology also contributes to cyber-physical systems, such as smart grid and connected vehicles, which requires enhanced security protection in today’s connected world