Simulation-based Risk Research Articles

Importance Sampling for Cost-Optimized Estimation of Burn Probability Maps in Wildfire Monte Carlo Simulations

Background: Wildfire modelers rely on Monte Carlo simulations of wildland fire to produce burn probability maps. These simulations are computationally expensive. Methods: We study the application of importance sampling to accelerate the estimation of burn probability maps, using L2 distance as the metric of deviation. Results: Assuming a large area of interest, we prove that the optimal proposal distribution reweights the probability of ignitions by the square root of the expected burned area divided by the expected computational cost and then generalize these results to the assets-weighted L2 distance. We also propose a practical approach to searching for a good proposal distribution. Conclusions: These findings contribute quantitative methods for optimizing the precision/computation ratio of wildfire Monte Carlo simulations without biasing the results, offer a principled conceptual framework for justifying and reasoning about other computational shortcuts, and can be readily generalized to a broader spectrum of simulation-based risk modeling.

Assessment of risk for aromatic hydrocarbons resulting from subsea Blowouts: A case study in eastern Canada

There is increasing concern over the environmental risks associated with deepwater petroleum exploration activities. The integration of environmental risk assessment and oil spill modeling can help to understand and quantitatively characterize the potential risks from subsea blowouts in specific regions. This study integrates a novel deepwater oil spill model (DWOSM) and an extended stochastic modeling methodology to assess the environmental risk of polycyclic aromatic hydrocarbons (PAHs) during a simulated offshore subsurface blowout off the east coast of Newfoundland, Canada. Additionally, the effectiveness of subsea dispersant injection (SSDI) in spill mitigation was investigated through comparative simulations. Resultant spill hazard and risk maps for current and proposed areas of offshore oil and gas development, in support of contingency plans, revealed that surfaced oil tends to drift toward the southeast and east in the hypothetical blowout case; nearshore areas of east Newfoundland have relatively low risk shortly after a deep-sea blowout; released PAHs may elicit more adverse ecological impacts than volatile organic compounds (VOCs); and SSDI application can reduce contaminant exposure levels but at the expense of enlarging the impacted zone for a short term. This stochastic simulation-based risk assessment provides scientific evidence to support decision-making in strategic oil spill response operations.

Logic-driven, simulation-based risk engineering to ensure the sustainability of productive processes even with data scarcity

Designing and operating production systems and keeping them up to date at the speed of innovation to meet competition, consumer trends and sustainability requirements is a challenging task. The problem becomes even more challenging when the production system is highly uncertain and data-poor. This complexity is increasingly recognised, as is the value that risk engineering can bring to the design, operation, maintenance and upgrading of production systems to avoid compromising their viability and ensure their continued efficiency.The manuscript is guided by this challenge and has a twofold objective. First, it analyses and explains the importance of adopting a logic-driven and simulation-based risk engineering approach to support decision-making. This objective is achieved by going to the root of the methodological constructs that characterise the methodologies available in the literature and by highlighting the main limitations. Second, it applies the HoRAM method to the use case of food banks to verify its suitability. The use case of food banks was chosen because it is characterised by more and unusual uncertainties compared to conventional production systems (such as uneven labour and raw material availability).The main contributions of this research are as follows. First, it provides a critical analysis of currently available risk assessment methodologies. It highlights the weaknesses and explains the implications of these weaknesses for decision support. Second, it concerns the development of a complete risk engineering process, offering an approach that goes beyond conventional risk assessment approaches that stop at the identification of the critical elements associated with the problem at hand. It explains the importance of closing the risk analysis loop by quantitatively verifying the efficiency of the identified mitigation solutions, thus moving from risk assessment to risk engineering.The results suggest that the proposed approach is suitable for supporting complex decision-making processes characterised by high uncertainty and data scarcity. Furthermore, due to the logic-driven nature of the proposed approach, non-experts can be involved and contribute to the analysis and engineering of risks. This allows to increase the situational awareness of decision makers and consequently their efficiency in making complex decisions.

A simulation-based risk interaction network model for risk management in international construction projects

The inherent complexity in International Construction Projects (ICPs) brings forth the intricate interactions between risks, invalidating the risk analysis under the conventional perception of independent risks. The diverse phases of ICPs feature the temporal dynamics of risks and interactions, exacerbating the challenge of pinpointing critical risks. This study aims to model how the risks are interconnected and amplified by proposing an ICP risk management framework, in which the project phase-based Risk Interaction Network (RIN) is constructed. An integrated index is designed to evaluate each risk’s criticality and discern key risks within a two-part procedure, where the simulation-based model estimates risk sensitivity to predecessor risks and captures the stochasticity of risk occurring, and network analysis gauges risk spread power on the entire network. An example of an application is provided to illustrate the utility of the proposed framework.

Simulation-based seismic risk and robustness assessment of aging bridge networks

Simulation-based seismic risk and robustness assessment of aging bridge networks

Monte Carlo Simulation-Based Risk Assessment for Unmanned Ground Equipment Taxiing Guidance

Monte Carlo Simulation-Based Risk Assessment for Unmanned Ground Equipment Taxiing Guidance

Sustainable planning and design for eco-industrial parks using integrated multi-objective optimization and fuzzy analytic hierarchy process

ABSTRACT The increasing growth of industrial facilities calls for sustainable planning and design. In this research, the multiple objective optimization model is developed to plan for Eco-Industrial Parks, in which economic, social, and environmental criteria are evaluated. Our proposed optimization model is integrated with the simulation-based risk profile based on emergency scenarios. Varied chemical substances and explosive locations are further analyzed to assess the impact of risk. Then, the Fuzzy Analytic Hierarchy Process technique is applied to examine uncertain decisions. Next, this study presents the proposed model with the case study of selective industrial-building types in an industrial park, in which industrial plants, hazardous material plants, warehouse buildings, and office space are included. Our analysis reveals that tradeoffs exist in designing an industrial estate with various industrial plants under conflicting objectives. Finally, tradeoffs are investigated using Pareto analysis resulting in sustainable planning for an industrial estate.

Discrete Event Simulation-Based Risk Analysis for Efficient, Sustainable and Resilient Transportation


 
 
 In a resource-constrained world the avoidance of transport-related emissions is crucial to fulfill the sustainable development goals, the Paris agreement on climate change and the Aichi biodiversity targets. Transport simulation enables the development of transport fleet strategies in risk scenarios to further improve efficientcy, sustainability and resilience of supply chains. Discrete event simulation is a valuable methodology for transport simulation, because it focuses on business processes for a digital representation of supply chains and provides an intuitive approach for facilitating stakeholder participation. Consequently, discrete event simulation models for unimodal, multimodal and multi-echelon unimodal wood transport are presented, which enable multicriteria-based strategy development, optimal fleet configuration as well as risk management and wood quality preservation. Furthermore, the verificated and validated discrete event simulation models were used in game-based stakeholder workshops to establish credibility, provide hands- on decision support and facilitate knowledge transfer between science, industry and education. A previous version of this revised and extended journal article was published as conference paper in the Proceedings of the XIVth Junior Forensic Science Conference (JuFoS), 18–19 May 2023 in Brno.
 
 

Economic Analysis of an Integrated Steel Plant Equipped with a Blast Furnace or Oxygen Blast Furnace

The oxygen blast furnace with top gas recycling (TGR-OBF) ironmaking technology can reduce CO2 emissions, especially when combined with carbon capture and storage technology (CCS). However, the successful commercialization of technology cannot be achieved without economic evaluation. This study applied the Box–Behnken design method and a Monte Carlo simulation-based risk analysis to assess the sensitivity of influencing factors affecting the net present value (NPV) of an integrated steel plant (ISP) and to predict the impact of variable market scenarios on the NPV of three ISPs. The results indicated that among the three ISPs, the conventional ISP (C-ISP) is the least profitable, followed by the ISP equipped with TGR-OBF and CCS (ISP-OBF-CCS), and the most profitable is the ISP equipped with TGR-OBF (ISP-OBF), which is at least CNY 0.392 Bn and CNY 1.934 Bn more profitable than the ISP-OBF-CCS and C-ISP respectively. Under the current Chinese carbon trading policy and the cost of CCS, CCS technology does not make a company profitable. This study explored an approach for analyzing ISP economic applicability under uncertain markets, which can be used as a reference for the development of alternative processes for steel production.

Error propagation for quantile estimation via combining Polynomial Chaos expansions and metalog distributions

Introducing parametric uncertainties to models is a quintessential step of simulation-based risk assessment and rare event simulations. A novel quantile estimation method is proposed based on generalized Polynomial Chaos expansions and metalog distribution estimation. We propose to derive the metalog distribution based on the statistical information of an expansion of the random model. The advantage of latter lies in a reduced number of evaluations of the full model, while deriving the metalog distribution avoids sampling-related challenges of quantile estimation. Error estimates and an algorithm to choose the relevant number of statistical moments are developed and analyzed, giving a framework for assessing the method applicability and the accuracy of the quantile estimation. The proposed method and the analysis are demonstrated on numerical examples. The method is applied to a complex fluid flow problem in transpiration cooling.

Simulation-based risk assessment for the leakage of toxic substances in a chemical plant and the effects on the human body: ethanol as a working model

Chemical plants must handle a wide variety of hazardous substances. To ensure safety in such plants, it is necessary to conduct extensive and highly accurate risk assessments. In this study, we aimed at developing a method that enables flexible and accurate risk assessment. We combined two different simulation tools to reproduce the phenomena of toxic gas leakage and diffusion as well as its impact on human health. The atmospheric diffusion after the leakage of toxic gas was simulated by computational fluid dynamics (CFD). Assuming the movement line of the person, toxic gas absorption and subsequent metabolism were calculated by a physiologically based pharmacokinetic (PBPK) model. From this, changes in blood concentration of toxic substances with time were simulated and we evaluated the effects of toxic gases on human body. Ethanol was selected as a toxic gas in this study. Based on the assumed scenario, the diffusion of leaked ethanol gas was calculated by CFD leading to the confirmation that the concentration of ethanol gas varies significantly with wind speed, human position, and elapsed time. The PBPK model showed that the maximum blood concentration of ethanol was 161 µmol/L, which is sufficiently low compared to that of ethanol poisoning (i.e., 10,900 µmol/L). These results suggest that the effects on the human body are relatively low and the evacuation can be performed safely. Compared to conventional methods of risk assessment, our new method allows the risk assessment of multiple scenarios, namely interindividual differences, activity status and the used of protective equipment.

COVID-19 aerosol transmission simulation-based risk analysis for in-person learning.

As educational institutions begin a school year following a year and a half of disruption from the COVID-19 pandemic, risk analysis can help to support decision-making for resuming in-person instructional operation by providing estimates of the relative risk reduction due to different interventions. In particular, a simulation-based risk analysis approach enables scenario evaluation and comparison to guide decision making and action prioritization under uncertainty. We develop a simulation model to characterize the risks and uncertainties associated with infections resulting from aerosol exposure in in-person classes. We demonstrate this approach by applying it to model a semester of courses in a real college with approximately 11,000 students embedded within a larger university. To have practical impact, risk cannot focus on only infections as the end point of interest, we estimate the risks of infection, hospitalizations, and deaths of students and faculty in the college. We incorporate uncertainties in disease transmission, the impact of policies such as masking and facility interventions, and variables outside of the college's control such as population-level disease and immunity prevalence. We show in our example application that universal use of masks that block 40% of aerosols and the installation of near-ceiling, fan-mounted UVC systems both have the potential to lead to substantial risk reductions and that these effects can be modeled at the individual room level. These results exemplify how such simulation-based risk analysis can inform decision making and prioritization under great uncertainty.

A stochastic simulation-based risk assessment method for water allocation under uncertainty

Abstract There are a lot of uncertainties in the water resources system, which makes the water allocation plan very risky. In order to analyze the risks of water resources allocation under uncertain conditions, a new methodology called the stochastic simulation-based risk assessment approach is developed in this paper. First, the main hydrological stochastic variable is fitted by a proper probability distribution. Second, suitable two-stage stochastic programming is constructed to obtain the expected benefit and optimized water allocation targets. Third, the Monte Carlo method is used to obtain a suitable stochastic sample of the hydrological variable. Fourth, a pre-allocated water optimization model is proposed to obtain optimized actual benefit. The methodology can give a way for risk analysis of water allocation plans obtained by uncertain optimization models, which provides reliable assistance to water managers in decision-making. The proposed methodology is applied to the Zhanghe Irrigation District and the risk of the water allocation plan obtained under the randomness of annual inflow is assessed. In addition, three different division methods of the annual inflow are applied in the first step, namely three levels, five levels and seven levels, respectively. From the results, the risk of the water allocation scheme obtained by the TSP model is 0.372–0.411 and decreases with the increase of the number of hydrological levels. Considering both the risk and model complexity, seven hydrological levels are recommended when using the TSP model to optimize water allocation under stochastic uncertainty.

Maintenance Activities Optimization via Modelling Dedicated to Manufacturing-Distribution Systems: Selected Case Studies Discussion

Optimizing the maintenance activities coordination-scheduling can be a daily challenge in any technical system and becomes more difficult when the size of the system is large and economic and logistic variables are involved. In general, as we can guess, the coordination-scheduling process modelling can be defined as an optimization problem once the target or goal to be met and the system model is well known. In order to address the above statements, well-defined methodologies have been proposed to model any coordination-scheduling process. Among them, simulation-based risk assessment is a comprehensive, flexible, and adaptable methodology to solve this problem. In this paper, we generalize a risk assessment approach and analyze case studies in different domains where the methodological model remains the same, but the parameters, variables and system under study are constantly changing. The article builds, based on different case studies, a general, adaptive, and simple simulation-based risk assessment model to optimize the coordination scheduling of maintenance tasks, which is able to intuitively and easily calibrate data-driven variables and parameters using a machine learning approach. Maintenance activities optimization via modelling approach is dedicated to manufacturing-distribution systems and is presented based on selected case studies.

Simulation-Based and Risk-Informed Assessment of the Effectiveness of Tsunami Evacuation Routes Using Agent-Based Modeling: A Case Study of Seaside, Oregon

Typically, tsunami evacuation routes are marked using signs in the transportation network and the evacuation map is made to educate people on how to follow the evacuation route. However, tsunami evacuation routes are usually identified without the support of evacuation simulation, and the route effectiveness in the reduction of evacuation risk is typically unknown quantitatively. This study proposes a simulation-based and risk-informed framework for quantitative evaluation of the effectiveness of evacuation routes in reducing evacuation risk. An agent-based model is used to simulate the tsunami evacuation, which is then used in a simulation-based risk assessment framework to evaluate the evacuation risk. The route effectiveness in reducing the evacuation risk is evaluated by investigating how the evacuation risk varies with the proportion of the evacuees that use the evacuation route. The impacts of critical risk factors such as evacuation mode (for example, on foot or by car) and population size and distribution on the route effectiveness are also investigated. The evacuation risks under different cases are efficiently calculated using the augmented sample-based approach. The proposed approach is applied to the risk-informed evaluation of the route effectiveness for tsunami evacuation in Seaside, Oregon. The evaluation results show that the route usage is overall effective in reducing the evacuation risk in the study area. The results can be used for evacuation preparedness education and hence effective evacuation.

Built Environment Typologies Prone to Risk: A Cluster Analysis of Open Spaces in Italian Cities

Planning for preparedness, in terms of multi-hazard disasters, involves testing the relevant abilities to mitigate damage and build resilience, through the assessment of deterministic disaster scenarios. Among risk-prone assets, open spaces (OSs) play a significant role in the characterization of the built environment (BE) and represent the relevant urban portion on which to develop multi-risk scenarios. The aim of this paper is to elaborate ideal scenarios—namely, Built Environment Typologies (BETs)—for simulation-based risk assessment actions, considering the safety and resilience of BEs in emergency conditions. The investigation is conducted through the GIS data collection of the common characteristics of OSs (i.e., squares), identified through five parameters considered significant in the scientific literature. These data were processed through a non-hierarchical cluster analysis. The results of the cluster analysis identified five groups of OSs, characterized by specific morphological, functional, and physical characteristics. Combining the outcomes of the cluster analysis with a critical analysis, nine final BETs were identified. The resulting BETs were linked to characteristic risk combinations, according to the analysed parameters. Thus, the multi-risk scenarios identified through the statistical analysis lay the basis for future risk assessments of BEs, based on the peculiar characteristics of Italian towns.

A simulation-based risk interdependency network model for project risk assessment

Project risks are mostly considered to be independent in risk management, ignoring interdependencies among them, which can lead to inappropriate risk assessment and reduced efficacy in risk treatment. This study introduces a new Monte Carlo simulation-based risk interdependency network model to support decision makers in assessing project risks and planning risk treatment actions more effectively. The Interpretive Structural Modeling method is integrated into this model to develop a hierarchical project risk interdependency network based on identified risks and their cause-effect relationships. The Monte Carlo method is used to model the stochastic behavior of risk occurrence and to generate numerous possible risk scenarios through simulation. To evaluate single risks and overall project risk level while considering risk interdependencies, five major risk indicators are therefore proposed, namely simulated occurrence probability of a risk, simulated local and global influence of a risk, as well as total risk loss and total risk propagation loss of a project. An additional sensitivity analysis is also included to examine the effects of input uncertainties of this model on risk assessment results. Moreover, two case studies are provided to demonstrate the application and effectiveness of the proposed model. The findings accentuate the significance of considering risk interdependencies in project risk assessment and validate the model's robustness and feasibility in risk management of projects with complex interrelated risks.

Feasibility of zero-energy affordable housing

The United States needs comprehensive policymaking reforms that promote sustainable residential development. This study investigates the feasibility of statewide, zero-energy affordable housing by analyzing historical data on the climate, energy use, and solar system costs in the Commonwealth of Virginia. The hypothesis examined is the net present cost of implementation of rooftop residential solar systems to achieve zero-energy, multifamily buildings of the U.S. Low Income Housing Tax Credit (LIHTC) program is lower than the discounted present cost of energy of otherwise identical buildings that run without renewable energy generation systems. We propose a generalizable framework for analyzing the feasibility of achieving regionwide or statewide zero-energy affordable housing. To validate the framework, we employ a longitudinal sample of monthly energy use data from 2013 to 2016, obtained from 310 residential units of 15 LIHTC developments across Virginia. Based on statistical regression analysis, energy simulation, and simulation-based risk analysis, we find that the net present value of investments in zero-energy affordable housing can be positive with a low risk. The investment value varies often depending on the zero-energy building definition, weather characteristics, retail price of electricity, and incentive rate. The results suggest that many other southern states can accrue benefits from zero-energy affordable housing.

Accounting for directivity-induced pulse-like ground motions in building portfolio loss assessment

Earthquake-induced pulse-like ground motions are often observed in near-source conditions due to forward-directivity. Recent worldwide earthquakes have emphasised the severe damage potential of such pulse-like ground motions. This paper introduces a framework to quantify the impact of directivity-induced pulse-like ground motions on the direct economic losses of building portfolios. To this aim, a simulation-based probabilistic risk modelling framework is implemented for various synthetic building portfolios located either in the fault-parallel or fault-normal orientations with respect to a case-study strike–slip fault. Three low-to-mid-rise building typologies representative of distinct vulnerability classes in the Mediterranean region are considered: non-ductile moment-resisting reinforced concrete (RC) frames with masonry infills, mainly designed to only sustain gravity loads (i.e. pre-code frames); moment-resisting RC infilled frames designed considering seismic provisions for high ductility capacity (i.e. special-code frames); special-code steel moment-resisting frames. Monte Carlo-based probabilistic seismic hazard analysis is first performed, considering the relevant modifications to account for the pulse-occurrence probability and the resulting spectral amplification. Hazard curves for sites/buildings located at different distances from the fault are obtained, discussing the spatial distribution of the hazard amplification. A set of pulse-like ground motions and a set of one-to-one spectrally-equivalent ordinary records are used to perform non-linear dynamic analysis and derive fragility relationships for each considered building typology. A vulnerability model is finally built by combining the derived fragility relationships with a (building-level) damage-to-loss model. The results are presented in terms of intensity-based and expected annual loss for synthetic portfolios of different sizes and distribution of building types. It is shown that, for particularly short-period structures (e.g. infilled RC frames), the influence of near-source directivity can be reasonably neglected in the fragility derivation while kept in place in the hazard component. Overall, near-source directivity effects are significant when estimating losses of individual buildings or small portfolios located very close to a fault. Nevertheless, the impact of pulse-like ground motions on losses for larger portfolios can be considered minimal and can be neglected in most of the practical large-scale seismic risk assessment applications.

Life-cycle management of deteriorating bridge networks with network-level risk bounds and system reliability analysis

Structural deterioration poses a substantial threat to the safety, serviceability, and functionality of bridges. Since bridges are connected in transportation networks, their failure can dramatically alter traffic flow, causing immense social consequences such as large-scale traffic delay and additional vehicle operating cost. In this paper, a novel method is proposed for life-cycle management of deteriorating bridge networks. The proposed method aims at minimizing network-level risks associated with deterioration-induced bridge failure. To combat the deficiencies of existing Monte Carlo simulation-based risk assessment methods, the proposed method adopts a non-simulation approach that relies on risk bounds of deteriorating bridge networks. In particular, this method uses system reliability analysis to determine the occurrence probabilities of various bridge failure scenarios in a network. For each failure scenario considered, traffic assignment is conducted to predict the traffic flow in the damaged network and the network-level consequences. The upper and lower bounds of the network-level risk are then formulated, allowing for efficient and accurate risk assessment with only a few traffic assignment operations. Estimated by the average value of its upper and lower bounds, the network-level risk is used as the optimization objective in a metaheuristic search procedure to obtain optimal life-cycle maintenance plans including the maintenance schedule and the investment on each maintenance action. The proposed method excels in its ability to account for the impacts of unlikely, yet high-impact events, as well as its ability to obtain optimal or near-optimal life-cycle maintenance plans that can more effectively reduce risks than those obtained using simulation-based methods.