In the ever-evolving field of environmental earth sciences, the stability and safety assessment of critical infrastructure such as earth-rock dams remain a priority for researchers and engineers alike. A recent breakthrough study conducted by Li, Yin, Zhang, and their colleagues introduces an innovative approach to understanding and mitigating the risks associated with earth-rock dam failures. Their pioneering work, published in Environmental Earth Sciences, dives deep into the complex interplay of factors that may lead to catastrophic dam breaks, utilizing a sophisticated hybrid model that integrates the interpretive structural modeling (ISM) technique with Bayesian networks (BN). This approach unlocks new analytical pathways in risk factor coupling that have the potential to revolutionize dam safety management.
Earth-rock dams, though economical and widely constructed across various georegions, pose unique challenges due to their heterogeneous materials and susceptibility to environmental and operational stressors. Traditional modeling techniques have often fallen short in capturing the multi-dimensional, interrelated risks that culminate in dam failures. Li et al.’s study tackles this issue head-on by employing an ISM-BN model that methodically deciphers the hierarchy and causal relationships among diverse risk elements, ultimately providing a probabilistic risk framework grounded in empirical data and expert judgment.
At its core, interpretive structural modeling (ISM) serves as a powerful method for structuring complex systems by decomposing and arranging variables into a multi-level hierarchical structure. The researchers first used ISM to map out an exhaustive list of earth-rock dam break risk factors identified from extensive literature review and expert consultations. These factors encompass geotechnical properties, hydrological influences, seismic activity, construction quality, maintenance deficiencies, and emergency response limitations, reflecting the multifaceted nature of dam safety challenges.
Following the structured framework established by ISM, the Bayesian network (BN) was employed to capture the probabilistic dependencies among the risk factors. Bayesian networks excel at representing conditional dependencies and facilitating inference under uncertainty, which is particularly valuable in systems like earth-rock dams where direct measurements are limited or imprecise. The BN model allows dynamic updating of failure probabilities as new information becomes available, enabling real-time risk assessment and targeted preventive actions.
One of the significant contributions of this research lies in the coupling analysis enabled by the ISM-BN hybrid framework. By integrating the hierarchical clarity of ISM with the probabilistic reasoning strength of BN, the authors identified critical risk pathways that are not apparent when considering factors in isolation. For example, the interplay between geotechnical weaknesses and extreme rainfall events amplifies failure probability disproportionately, underscoring the need for integrated monitoring systems that simultaneously track multiple parameters.
Moreover, the study highlights the dynamic nature of risk factors over the lifespan of an earth-rock dam. The model incorporates temporal variability in maintenance schedules, sedimentation rates, climatic fluctuations, and seismic hazard exposure. This temporal dimension offers a nuanced understanding of how certain risk interactions evolve, thereby informing adaptive management strategies that can preemptively address emerging vulnerabilities before they culminate in failure.
The integration of expert knowledge within the ISM-BN model also marks a methodological advancement. Recognizing data scarcity challenges in dam safety research, the authors devised a systematic approach for eliciting and quantifying expert judgments to enrich the Bayesian network’s conditional probability tables. This fusion of empirical evidence and expert insight enhances model robustness and credibility, particularly under scenarios where monitoring data are sparse or uncertain.
From a broader engineering perspective, the ISM-BN model’s capability to systematically dissect and predict earth-rock dam break risks presents opportunities for policymakers and dam operators to optimize resource allocation. Risk-informed decisions guided by this model can prioritize inspection frequencies, retrofit measures, and emergency planning protocols tailored to the most sensitive risk nodes identified in the coupling analysis. This targeted approach is not only cost-effective but also enhances public safety by reducing uncertainties in dam break predictions.
Interestingly, the applicability of the ISM-BN framework extends beyond earth-rock dams. The authors suggest that their methodology could be adapted for other complex infrastructure risk analyses, such as levees, embankments, and even urban flood defenses, where risk factors are similarly multifactorial and interdependent. The cross-disciplinary potential of this approach broadens its impact in resilience engineering and disaster risk reduction sciences.
It is important to note that while the ISM-BN model advances the sophistication of dam break risk assessment, the researchers acknowledge limitations related to model parameter sensitivity and the quality of expert input. Future work is recommended to integrate real-time sensor networks and remote sensing data to continually refine the Bayesian network probabilities, thereby improving predictive accuracy and operational relevance.
The study’s detailed case analyses of specific earth-rock dams reveal distinct risk signatures driven by site-specific geological, climatic, and infrastructural characteristics. This localized modeling capability ensures that generic risk factors do not obscure unique vulnerabilities, reinforcing the importance of tailored safety management frameworks at individual dam sites.
Furthermore, the ISM-BN model’s visualization tools provide intuitive mapping of causal links and risk propagation, enabling clearer communication with stakeholders from engineers to community leaders. By demystifying complex risk interdependencies, the model fosters collaborative risk mitigation efforts and enhances community resilience-building initiatives in dam-adjacent regions.
In the context of global climate change, intensified hydrological cycles pose escalating threats to earth-rock dam safety worldwide. The study underscores that models like the ISM-BN must evolve to incorporate climate projections, ensuring that risk assessments remain relevant under future environmental scenarios. This forward-looking capability could substantially influence the design and regulation of new dams and the retrofit of existing structures.
Overall, Li and collaborators’ contribution represents a paradigm shift in dam break risk analysis, moving from static, fragmented assessments to a dynamic, integrated, and probabilistic approach. Their work exemplifies how advanced computational modeling, combined with expert knowledge and systems thinking, can deliver actionable insights for safeguarding critical infrastructure and protecting lives.
As the field of environmental earth sciences continues to address increasingly complex infrastructure challenges, such innovative hybrid modeling frameworks pave the way for more resilient and adaptive engineering solutions. The coupling analysis of earth-rock dam break risk factors through the ISM-BN model not only enriches academic understanding but also serves as a vital tool for practitioners, governments, and communities striving to coexist safely alongside these massive yet vulnerable human-made constructs.
Subject of Research: Coupling analysis of risk factors contributing to earth-rock dam failure risk.
Article Title: Coupling analysis of earth-rock dam break risk factors based on the ISM-BN model.
Article References:
Li, Y., Yin, Q., Zhang, Y. et al. Coupling analysis of earth-rock dam break risk factors based on the ISM-BN model. Environ Earth Sci 84, 488 (2025). https://doi.org/10.1007/s12665-025-12495-8
Image Credits: AI Generated
