On February 2007, a massive fire in a propane de-asphalting unit in an oil refinery in Texas, USA happened due to liquid propane release from a cracked pipe in a control station injuring four people, damaging extensive equipment, causing significant business interruption, and resulting in more than $50 million losses. The accident was triggered by a natural hazard: freezing of piping at a control station caused an inlet pipe elbow to crack, which in turn, led to the release of high-pressure liquid propane which was rapidly ignited. In addition, there were two near-miss events due to potential domino effects. In fact, the accident could reasonably have resulted in much more severe consequences due to the exposure of large butane storage spheres and chlorine containers, increasing the possibility of a catastrophic domino effect. This paper develops a Natech (natural hazard triggering technological disasters) risk assessment methodology that relies upon Bayesian network capabilities and takes into account the potential Natech domino effects. The methodology is implemented in the intended refinery and mathematically graphically represents the dynamic cause–effect relations between units involved in the scenario, and handles uncertainties among the interactions. In addition, the methodology can provide a risk value for the entire scenario that can be used further for risk-based decision making.@en

On August 25, 2017, Hurricane Harvey made landfall near Rockport, Texas as a Category 4 hurricane with maximum sustained winds of approximately 200 km/hour. Harvey caused severe damages in coastal Texas due to extreme winds and storm surge, but will go down in history for record-setting rainfall totals and flood-related damages. Across large portions of southeast Texas, rainfall totals during the six-day period between August 25 and 31, 2017 were amongst the highest ever recorded, causing flooding at an unprecedented scale. More than 100,000 residential properties are estimated to have been affected in southeast Texas. It is likely that Harvey will rank among the costliest storms in U.S. history. In the wake of Hurricane Harvey, Delft University of Technology has initiated a Harvey Research Team to undertake a coordinated multidisciplinary investigation of the events with a focus on the greater Houston area. This ‘fact-finding’ research is based on information available from public sources during and in the first weeks after the event. Results are therefore preliminary, but aim to provide insight into lessons that can be learned for both Texas and the Netherlands. As part of the investigations, a hackathon with more than 80 participants was organized to collect and analyze available public information. Houston was especially hard hit by flooding. During the event, all 22 watersheds in the greater Houston area experienced flooding. Many of Houston’s creeks and bayous exceeded their channel capacities, reaching water levels never before recorded. Across large portions of Harris County, rainfall totals exceeded the 1000-year return period. In addition, the water from the two reservoirs protecting downtown Houston (Addicks and Barker) were opened on August 28 to prevent catastrophic damages to the dams and further flooding in upstream communities. The releases exacerbated flooding in the areas downstream of the dams and an estimated 4,000 homes in neighborhoods downstream of the dams were impacted by flooding. The consequences of the event in the greater Houston area have been characterized in terms of economic damages, loss of life and impacts on critical infrastructure, airports and industry. In total, more than 100,000 homes were affected more than 70 fatalities were reported in the greater Houston area. The event highlighted the vulnerability of industrial facilities, as several cascading impacts (releases of toxic materials and explosions) were reported. Emergency response has been assessed. No large-scale mandatory evacuation was ordered before or during Harvey. However, it appeared that several local evacuations were ordered for areas with specific risks and circumstances. During the event, many people were trapped by rising waters necessitating a major rescue operation. In total, more than 10,000 rescues were made by professional and volunteer rescuers. Social media played an important role during the event and recovery, as an additional source of information, to inform emergency managers and as a means to organize community response e.g. for clean-up. Also, messages were conveyed through social media, e.g. a report of a levee breach that appeared to be incorrect afterwards. Major flooding is a problem that has multiple causes from both physical and social origin. Based on the investigations, recommendations for future research and lessons for flood management have been formulated. A better understanding of the issues studied in this report is expected to contribute to a knowledge basis for further in-depth investigations and future directions for flood risk reduction. Data collection and Report production funded by DIMI and DSys Special Case 'Houston Galveston Bay Region, Texas, USA' Project 'Harvey hackathon' and follow-up research @en

Major accidents in Western countries, receiving a lot of media attention in the 1970s, are starting point for research into internal and external domino effects in the chemical and petrochemical sectors and clusters. Initially, these reports are published by government institutions and government-related research centers. With the upcoming quantitative risk analyses in the 1970 and 1980s, the so-called colored books, published in the Netherlands, play a prominent role in quantifying these domino effects. Since the mid-1990s, the second European Seveso Directive encourages scientific research on domino effects, shown in substantial growth of academic publications on the topic. Research in Western countries is dominated by risk assessments, probabilities, and failure mechanisms are calculated for the complex phenomenon of domino effects and its consequences. Previous works are closely related to political, official, and private decision-making.@en

Forest fires threaten a large part of the world's forests, communities, and industrial plants, triggering technological accidents (Natechs). Forest fire modelling with respect to contributing spatial parameters is one of the well-known ways not only to predict the fire occurrence in forests, but also to assess the risk of forest-fire-induced Natechs. This study is a review of methods based on geospatial information system (GIS) for modelling forest fires and their potential Natechs that have been implemented all over the world. The present study conducts a systematic literature review of the methods used for forest fire susceptibility, hazard, and risk assessment, while dividing them into four general categories: (a) statistical and data-driven models; (b) machine learning models; (c) multi-criteria decision-making models, and (d) ensemble models. In addition, some forest fire detection techniques using satellite imagery are reviewed. A comparison is also conducted to highlight the research gaps and required future research. The results of the present research assist decision makers to select the most appropriate techniques according to specific forest conditions. Results show that data-driven approaches are the most frequently applied methods while ensemble approaches are more accurate.@en

Chemical industrial parks, being critical infrastructures, are susceptible to domino effects triggered by intentional attacks. Previous research on security risk management has mainly focused on using security measures to prevent intentional attacks, neglecting the effects of safety barriers. Safety barriers are able to reduce the potential consequences and decrease the attractiveness of chemical industrial parks to terrorists who aim to maximize the damage. From a systematic perspective, the potential consequence of intentional attacks is defined as the expected loss which is the sum-product of damage probability and consequence of installations. A consequence-based method including a Dynamic Vulnerability Assessment Graph (DVAG) model is proposed to integrate safety and security resources for reducing the risk of intentional attacks. The DVAG model is developed based on dynamic graphs, considering the effects of security measures, safety barriers, and emergency response. This method can assess the consequences and damage probabilities of possible intentional attacks so as to mitigate the risk via evaluation and allocation of security measures and safety barriers with fast computation speed.@en

Securing critical infrastructures is a complex task. Required information is usually scarce or inexistent, and experts’ judgments may be inaccurate and biased. In this paper, two methodologies dealing with data scarcity, imprecision, and uncertainty are presented: Evidential network and Credal network. Evidential network is a graphical technique based on Dempster-Shafer Theory to explicitly model the propagation of epistemic uncertainty among variables while Credal network is an extension of Bayesian network to deal with sets of probabilities, known as Credal sets, based on experts’ judgments. Both methodologies constitute robust frameworks to account for high degree of imprecision on data, producing informative results despite the low-informative input. In the present study, the power in expressing uncertainty of these two methodologies have been showed, and their differences have been described through their application to a case study of security vulnerability assessment. Results demonstrate the substantial equivalence of the two methodologies in prognostic analysis, thus, an approximate updating procedure of Evidential network through equivalent Credal network has been proposed, to overcome the lack of possibility to compute updating in the context of Dempster-Shafer Theory.@en

Securing critical infrastructures is a complex task. Required information is usually scarce or inexistent, and experts’ judgments may be inaccurate and biased. In this paper, two methodologies dealing with data scarcity, imprecision, and uncertainty are presented: Evidential network and Credal network. Evidential network is a graphical technique based on Dempster-Shafer Theory to explicitly model the propagation of epistemic uncertainty among variables while Credal network is an extension of Bayesian network to deal with sets of probabilities, known as Credal sets, based on experts’ judgments. Both methodologies constitute robust frameworks to account for high degree of imprecision on data, producing informative results despite the low-informative input. In the present study, the power in expressing uncertainty of these two methodologies have been showed, and their differences have been described through their application to a case study of security vulnerability assessment. Results demonstrate the substantial equivalence of the two methodologies in prognostic analysis, thus, an approximate updating procedure of Evidential network through equivalent Credal network has been proposed, to overcome the lack of possibility to compute updating in the context of Dempster-Shafer Theory.@en

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases.@en

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases.@en

Process plants can be potential targets to terrorist attacks with the aim of triggering domino effects. Compared to accidental domino effects where the possibility of having multiple primary events is very remote, man-made domino effects are likelier to be initiated from multiple units within the plant in order to increase the knock-on likelihood and thus causing maximum damage. In this regard, identification of critical units that - under attack - may lead to likelier and severer domino effects is crucial both to assess the vulnerability of process plants and subsequently to increase their robustness to such attacks. In the present work, we have applied graph theory and dynamic Bayesian network to identify critical units. Further, low-capacity utilization of process plants (e.g., by keeping some of the storage tanks empty) has been demonstrated as an effective strategy in the case of imminent terrorist attacks. As such, the robustness of the plant against intentional attacks can temporarily be increased while considering the cost incurred because of such a low-capacity utilization.@en

In this paper, a conceptual framework is developed to improve NaTech safety in the chemical industry. The concept is called EPIC, indicating that emphasis should be put on Education, learning and training, Proactive risk minimization and safety innovation, Intensified informed inspection and analysis, and Cooperation and transparency. Concrete initiatives addressing NaTech for every domain of the EPIC conceptual framework are given. The innovativeness of the EPIC framework resides in the potential of the simultaneous application of initiatives within the four areas of improvement (E, P, I, and C) to make chemical clusters much more resilient with respect to nature-related disasters. As such, the proposed EPIC framework may lead to a much-needed revolution of NaTech safety in the chemical industry.@en

In this paper, a conceptual framework is developed to improve NaTech safety in the chemical industry. The concept is called EPIC, indicating that emphasis should be put on Education, learning and training, Proactive risk minimization and safety innovation, Intensified informed inspection and analysis, and Cooperation and transparency. Concrete initiatives addressing NaTech for every domain of the EPIC conceptual framework are given. The innovativeness of the EPIC framework resides in the potential of the simultaneous application of initiatives within the four areas of improvement (E, P, I, and C) to make chemical clusters much more resilient with respect to nature-related disasters. As such, the proposed EPIC framework may lead to a much-needed revolution of NaTech safety in the chemical industry.@en

he present study is to point out the outcomes of the Sem-inar on the Chemical Weapon Convention and Chemical Safety and Security Management for Member States in the Asia Region held by Organization for the Prohibition of Chemical Weapons in Doha, Qatar, in February 2017. The seminar was aimed at supporting chemical safety and secu-rity (CSS) management in the chemical industry in Asian countries. Overall goal was to sensitize States Parties to the new approaches that can be adopted in relation to CSS management, with a particular focus on providing assis-tance to small and medium-sized enterprises. This article reflects the observations made by keynote speakers via their interaction with participants from Asian State Parties dur-ing presentations’ questions & answers sessions and follow-ing workshops. The article is an attempt to highlight the challenges in security risk assessment and management of chemical facilities and discuss some new trends for further improvements.@en

In the context of natural-technological (natech) accidents, flood-induced damage of chemical and process facilities have received relatively less attention partly due to the scarcity of experimental or high resolution field observations from one hand and partly due to the rarity of such events on the other hand. In the present study, we have investigated the possible damage of a variety of chemical and process vessels such as atmospheric storage tanks as well as pressurized vertical and horizontal vessels. We have introduced a methodology based on load-resistance relationships to assess the vulnerability of vessels in the form of fragility functions. While logistic regression is used to develop fragility functions for prevailing different failure modes, that is, floatation and buckling, a Bayesian network methodology has been developed to combine the fragility functions, taking into account common causes and conditional dependencies. The end results have been presented in the form of point estimate total failure probabilities of vessels subject to credible floods. The results of the present study illustrate that although the floatation is the most prominent failure mode in case of floods, the anchorage of target vessels (via bolts), prevent the vessel floatation in almost all cases. In addition, for un-anchored vessels, such as atmospheric storage tanks, filling the tanks at least with as much chemicals (hydrocarbon products or chemicals with a density ranging from 650 to 850 Kg/m3) as equal to the height of flood inundation would significantly reduce the probability of floatation. In case of shell buckling, the height of inundation seems to be a more influential factor than the flow velocity. Besides, it turned out that a small increase in the wall thickness of target vessels would notably decrease the probability of shell buckling.@en

The authors regret that an imprecise statement was made in this article, and wish to offer this Corrigendum as clarification. The authors would like to apologise for any inconvenience caused. Imprecise statement: Misuri et al. (2018, pp. 70) state in their work: “Differently from EN [evidential network], CN [credal network] can be used both to conduct forward analysis and to update probabilities”. This statement implies that EN cannot be used for belief updating and should be mapped into a corresponding CN for that purpose. Correction: The foregoing statement may be correct for conventional ENs that are based on Dempster's combination rule, but does not hold true for the EN developed by Simon and co-workers (2008, 2009) based on Bayesian network (BN) inference algorithms (herein, BN-based EN). Simon and Weber (2009) explicitly mention in their work that the developed EN can be used for belief updating: “The computation mechanism is based on the Bayes theorem, which is extended to the representation of uncertain information according to the framework of Dempster-Shafer theory. Specific evidence (Hard evidence) is modeled by a mass of 1 on one of the focal elements of the frame of discernment. Non-specific evidence (Soft evidence) corresponds to a mass distribution on the focal elements of the frame of discernment.” Proof: Fig. 1 displays the BN-based EN developed in Misuri et al. (2018) for security vulnerability assessment of a process plant where the belief masses have been updated given the evidence “Attack = Success”. Misuri et al. (2018) used the BN-based EN for predicting the probability of a successful attack (forward analysis), but to update the probabilities (backward analysis) they mapped the BN-based EN into an equivalent CN and calculated the updated probabilities given “Attack = Success”. They used two packages, JavaBayes and GL2U, to implement the CN, concluding that JavaBayes results in more consisting updated probabilities with regard to the evidence (the 2nd column in Table 1). In the present corrigendum, we used the BN-based EN (Fig. 1) for belief updating given “Attack = Success”. The updated beliefs were subsequently used to calculate updated probability intervals (the 3rd column in Table 1), showing a good agreement between the results of CN and BN-based EN. Conclusion: The EN developed by Simon and co-workers (2008, 2009) based on BN can be used for belief mass updating the same way BN can be used for probability updating, with no need for using CN.@en