Smart Tunnel and Dynamic Risk Analysis

The article proposes an innovative system of supervision and data acquisition, oriented to fire safety through a dynamic risk analysis, called supervisory control and dynamic risk analysis (SCADRA). SCADRA achieves operation and management benefits for safety, functionality, and energy optimization, by means of dynamic control and monitoring measures. A “Smart Tunnel” can be defined as a model where technical systems are designed and installed in an advanced way, by using digital components and sensors. SCADRA has been developed to offer a complete tool (human machine interface) for real-time monitoring of tunnel safety by tunnel operators, integrated with the innovative dynamic risk analysis. SCADRA offers compensatory and/or supplementary safety measures for emergency management and supports fire rescue teams that are operating in tunnel in which some requirements could not be realized.

Safety in tunnels is being contemplated through a holistic approach due to accidents that occurred through years. Technological innovations have led to the tunnel concept evolution from civil to technological infrastructure, where technology overcomes the limits of the geometric design, increasing its operating capacity. This paper aims to illustrate SCADRA principles (Supervisory Control Acquisition and Dynamic Risk Analysis), developed thanks to EURAM (EUropean Risk Analysis Method) methodology, which assessed the risk of over 600 road tunnel tubes, focusing on the following values: users and managers' needs (Human-centricity), usage of green technologies and application of energy saving strategy (Sustainability), and improvement of tunnels resilience (Resilience). SCADRA is used in monitoring continuously the tunnel state by gathering the safety influence variables and performing a dynamic risk analysis, quantitative and probabilistic, in real-time. The system was installed in three Italian road tunnels, executing over 130,000 instantaneous risk analyses. The applications’ results and the future developments will be presented according to tunnels’ safety improvement, maintenance, and management.

Technological innovations have led to the tunnel concept evolution from civil works to technological infrastructure, where technology overcomes the geometric-functional limits, increasing its operating capacity. Today, the tunnel system must have the ability to adapt to a scenario in which transportation is based on guided or self-driving cars, with internal combustion engines or electric motors, that safely run through Smart Roads and Smart Tunnels. This implies that the tunnel system must be part of “Intelligent Transport Systems” (ITS), a goal introduced by Directive 2010/40/EU [1]. This paper aims to illustrate the fundamental principles of SCADRA (Supervisory Control Acquisition and Dynamic Risk Analysis) system, developed and implemented thanks to EURAM (EUropean Risk Analysis Method) methodology, and how it can be empowered thanks to Artificial Intelligence (AI). SCADRA system is a supplementary prevention and safety measure for tunnels management, both during normal operation and in emergency conditions, capable of guaranteeing a real-time monitoring of tunnel risk level. Thanks to AI, the system can be enhanced in order to execute a predictive risk analysis, by assessing the tunnel risk level trend according to the data gathered and by adjusting the next prediction to the real-time risk data. Artificial Intelligence (AI) and the Generative AI (Gen AI) are concepts and areas of practical application that are quickly growing, in terms of interest, products and services across multiple sectors. The concept is also relevant to the road, transport and mobility sectors in areas such as infrastructure design, operations and management, but it could be crucial for the safety aspect.

10 - Dynamic risk analysis and risk model evaluation

Dynamic risk analysis of oil depot storage tank failure using a fuzzy Bayesian network model

The problem of risk update is one of the core problems of dynamic risk analysis. As for updating widely used probabilistic risk, it’s a kind of special problem of pattern recognition which means to transfer from old pattern to new pattern with new information. Aiming at updating probabilistic risk only with the new observations and without the original observations, this paper proposes the morphogenetic updating algorithm to update the probabilistic risk with only new observations and old risk values by reproducing the morphogenesis of new observations and old risk values based on morphogenetic estimation. The form rules hidden in the risk values and new observations are discovered by internal source in Write operation, and the projection of the input and the cosine similarity measure as the basis for defining the weight of updating are computed in Read operation. Based on Resconi theorem, the input is reproduced by its projection on the context which provides the same mathematical space for updating and stores the structural information hidden in the risk. By applying the algorithm to updating the probabilistic risk of typhoon rainstorms occurred in Guangdong Province, it proves that morphogenetic updating algorithm provides us a method to updating probabilistic risk fast and satisfactorily and it’s a sustainable updating method which can be used for dynamic risk analysis.

Structural health monitoring for bridge is becoming a more widely accepted way to improve bridge management. To implementation the above objective, this paper presents a GPRS-based bridge remote data acquisition and monitoring system. It is a kind of unmanned automation, intelligent data acquisition system. The network structure of the system, data transmission, information collection and data-processing model methods provide a common, advanced and effective information acquisition model for other supervisory control and data acquisition systems. This system are highly portable with little changes can be applied to other systems. These will not only improve the automation degree of monitoring, and also a lot of human and financial resources are saved with a high practical value and economic value.

This chapter consists of four sections outlining how to deal with hazards and ongoing risk management. First, we elaborate on how to approach an analysis of a system’s hazards and implement and maintain a hazard log. Next, we elaborate on how ongoing risk management and dynamic risk analysis should ensure that high risk systems are under continuous monitoring and regular review. In the process of hazard identification and risk management, having clearly formulated risk tolerability criteria is paramount. Therefore, Sect. 12.3 outlines several approaches one could use for this purpose. When addressing a high risk system’s possible hazards and risk management strategies, it is important to also ensure the effective management and integration of the specific rules, limitations, and constraints that must be followed and monitored when using the system. The last section therefore outlines on safety-related application conditions (SRAC).

Applications of smart technologies for automatic near miss detection in the industrial safety

In the digital era, effective business management relies on dynamic risk analysis and real-time data integration, particularly amid the evolving landscape shaped by technological advancements and external factors such as climate change and global health crises. This study delves into the specific demands for digital services within the transportation sector, focusing on the crucial task of identifying an optimal data-driven management system (platform) to bolster transportation decision-making processes. The paper revolves around the formulation of a comprehensive conceptual framework for the development of intelligent services and platforms tailored explicitly to transport enterprises. Methodologically, a thorough analysis of critical infrastructure-related challenges was conducted, emphasizing the integration of a service-oriented approach to enhance overall functionality. Central to the paper’s approach is the careful navigation of conflicting user requirements, resource constraints, and the imperative of maintaining adaptability in service implementation. Additionally, a robust data flow analysis framework is presented, encompassing data collection, model building, and model extrapolation, which enables the generation of reliable outputs essential for informed decision-making. Notably, the study underscores the pivotal role played by the EN.I.R.I.S.S.T. research infrastructure in delivering essential services to the transportation domain, offering accessible data, user-friendly interfaces, and data analysis tools. The findings highlight the enthusiastic reception of the diverse services among potential users, indicating a strong willingness to engage and benefit from the proposed solutions. By emphasizing the integration of intelligent services, the paper seeks to present a systematic approach aimed at enhancing the efficiency, productivity, and competitive edge of transport enterprises through the strategic deployment of advanced technological solutions and proactive planning. This paper ultimately contributes cutting-edge research insights, empowering transportation managers, planners, and decision-makers with valuable resources for informed business intelligence and corporate strategy.

In the last decade, high-technology industries have initiated numerous research projects to detect latent and emerging risks associated with their systems. These businesses are seeking to implement a proactive policy to handle industrial risks related to operating their systems in various contexts and conditions of use. This research has highlighted the value of dynamic risk analyses in mastering operational risks. Dynamic risk analysis is based on operating experience feedback using information collected about abnormal events, which are recorded to update the risk analyses done before the system in question was commissioned.

This article briefly reviews the theory of decision making under uncertainty and the programming methods used to consider uncertainty in farm planning. A multiperiod MOTAD model is developed to analyze the risk and return of a farm's investment financing, production, and marketing plans. The results of an empirical model of a beginning farmer in the Corn Belt are briefly presented to illustrate the potential applicability of the multiperiod MOTAD model and demonstrate the type of information generated. Most farm management decisions are made under conditions of uncertainty. This uncertainty in agriculture arises from market forces, weather, disease, insect damage, and other factors that cannot be controlled or accurately predicted. However, many agricultural economic models used to study farm management problems are specified under assumed certainty. For example, conventional linear programming used extensively in farm planning analyses does not accurately accommodate uncertainty. Of course, assumed yields and prices can be changed in these models to see how the farm organization is affected by changes in these variables. But there is no measurement of the risk associated with each farm organization. As a result, the conventional linear programming solutions for farm organization have often been rejected because, these solutions may specify actions that lead to a higher degree of risk than many farm managers are willing to accept [3, 5]. The objective of this article is to present a multiperiod linear programming model that can be used to evaluate farm management decisions under uncertainty. The discussion will begin with a review of the concepts of decision making under uncertainty. Then numerical analysis techniques will be discussed with emphasis on dynamic risk models. Finally, recent numerical results obtained from the use of a multiperiod MOTAD (Minimization of Total Absolute Deviations) model will be presented to illustrate its potential in dynamic risk analysis. DECISION MAKING UNDER UNCERTAINTY We assume that the objective of the rational individual is to maximize utility. Utility is derived from present and future consumption. Consumption, in turn, is a function of income. Utility can then be expressed as a function of income as:

Data collection and analysis is vital for evaluating the performance of any equipment or system. Though the methodology of collection, transmission, storage and retrieval varies in different scenarios, the basic components of the set-up remain the same. The Data Acquisition and Monitoring System evolved from the need for remote monitoring of facilities that did not have an independent reliable infrastructure to implement a full-fledged SCADA system. Back when IT infrastructure was prioritized for business communications within the limited bandwidth allocated, collection and transmission of process data through the available network infrastructure was a challenge. More so when there are concerns regarding the confidentiality of data being collected and transmitted. Individual facilities were provided with local instrumentation and controls for reliable operations. With the growing production rates, the focus generally goes for improving the processing facilities rather than reading, reporting or recording. Instead of an independent dedicated network for Supervisory Control and Data Acquisition system that mandate a huge infrastructural cost and dedicated skilled personnel, a pilot project was initiated with a minimum budget to test data transfer through existing communication network. Field transmitters were installed on Wellheads, Risers and Manifolds and Remote Terminal Units were provided to collect data from two platforms and to facilitate data transfer to the servers through existing microwave communication network with minimum field instrumentation. Considering bandwidth limitations and priority for business communications, servers in redundant configuration were placed strategically to ensure continuous data gathering. Pilot project was successful in providing reliable data and trends with minimum disruptions. Augmentation of DAMS network was envisioned to implement Digital Oil Field for monitoring multiple assets in real time. Implementation of Digital Oil Field continued with the addition of new field instrumentation and RTUs followed by server upgrades and integration of PLC based platform control systems to add four major producing platforms to the Data Acquisition and Monitoring System through existing microwave communication network. Upon successful commissioning, it was possible to gather data from various wellheads, process equipment and other independent meters for remote real time monitoring and analysis through user friendly HMI screens and customized dashboards to manage multiple assets effectively, improving performance evaluation, safety and reliability of operations.

In Green Internet of Things, energy consumption research is a hot topic. Our research focuses on Supervisory Control And Data Acquisition (SCADA) system, and it is a system consisting of a plurality of self-organized sensing networks. Live data gathering from numerous Sub-connected SCADA (S-SCADA) networks to make unified decisions based on collected data is one of pivot problem, which has not been well studied. In this article, an integral data-gathering framework is proposed to prolong network lifetime by using sink rotating and Unmanned Aerial Vehicles (UAV) path planning. In the proposed Sink Rotating joint UAV Data Gathering (SR-UAV-DG) framework, the data gathering process is divided into two organic components: (a) S-SCADA in-network data collection. The energy consumption of the network is balanced by selecting the node with the least energy consumption as the sink node. (b) Use UAV for data collection between S-SCADA networks. The theoretical analysis results show that the SR-UAV-DG framework proposed in this article reduces the maximum energy consumption of nodes in the network by 99.21% after 1000 rounds of data. The flight time of UAV is reduced by 16.83%. In the case of unreliable communication links, the data reception rate is guaranteed to reach 91.94%.

Event driven high speed data acquisition with IEEE 1588 synchronization for long pulse operations of Indian Test Facility for ITER DNB