How Riskion® Powers a New Risk-Informed Framework for Public Safety Around Dams

Public safety around dams has long relied on qualitative checklists, ordinal risk matrices, and prescriptive controls. While these approaches help identify... identify hazards, documenting public activities, and ranking risk, they often fall short when decision-makers must answer harder questions:

• Which risks truly matter most?
• Which controls deliver the highest safety impact per dollar?
• How much residual risk is acceptable—and why?

A newly published peer-reviewed research paper by Tareq Salloum and Ernest Forman offers a compelling answer—and validates the role of Riskion® as a practical decision-support platform for solving exactly these challenges.

Published in Civil Engineering Journal (MDPI, 2026), “A Risk-Informed Framework for Public Safety Around Dams” presents a quantitative, simulation-driven framework that bridges engineering judgment, public safety governance, and economic decision-making

Where Traditional Public Safety Risk Methods Can Be Improved

Most dam safety programs still rely on:

• Ordinal likelihood × consequence matrices
• Qualitative rankings (low / medium / high)
• Uniform application of physical controls (booms, fences, signage)

The paper draws on earlier research noting limitations in these methods. Ordinal scales cannot be multiplied meaningfully, and they fail to capture:

• Non-linear interactions between hazards, events, and impacts on objectives
• Overlapping controls
• Rare but high-consequence outcomes

As the authors note, this can lead to suboptimal prioritization and less‑efficient spending, even when intentions are good.

A Quantitative, Risk-Informed Alternative

The proposed framework integrates four core elements:

• Hazards (physical, activity-based, hydraulic)
• Events (e.g., loss of vessel control, drowning)
• Objectives (public safety, legal liability, public trust)
• Controls (physical, operational, educational)

These are connected using a bow-tie risk structure, weighted using the Analytic Hierarchy Process (AHP), and evaluated using Monte Carlo simulation to capture uncertainty and tail risk

Where Riskion Comes In

Rather than remaining a purely theoretical framework, the entire methodology is implemented and executed in Riskion® (version 6.19).

Specifically, Riskion is used to:

• Convert expert judgment into ratio-scale probabilities using AHP
• Model many-to-many relationships between hazards, events, and objectives
• Run 10,000+ Monte Carlo simulations to generate:

- Expected loss
- Loss Exceedance Curves (LECs)
- Value-at-Risk (VaR)

• Optimize control portfolios under real budget constraints

This transforms subjective judgment into traceable, auditable, and defensible quantitative outputs.

What the Case Study Revealed (And Why It Matters)

Using a representative dam site, the study uncovered several eye-opening insights:

1. Expected Public Safety Risk Was ~$11.35M Over 10 Years

Expressed using Value of Prevented Fatality (VPF), the baseline public safety risk equated to $11.35 million—a figure that immediately reframes safety discussions in decision-maker language.

2. Low-Cost Controls Outperformed Heavy Infrastructure

Some of the most cost-effective controls were:

• 24/7 video surveillance
• Operational procedures
• Signage and public communication

In contrast, expensive physical barriers alone delivered limited incremental benefit.

One control—24/7 video surveillance—reduced risk by ~$9.3M at a cost of $30K, a benefit-to-cost ratio exceeding 200:1

Optimization: From Safety Intuition to Safety Strategy

Optimization: From Professional Judgment to Safety Strategy

Riskion’s optimization engine identified an optimal control portfolio under a $50K budget that:

• Reduced total risk by ~$11M
• Lowered residual risk to ~2% of baseline
• Virtually eliminated 95th-percentile Value-at-Risk

This demonstrates a critical shift:

Public safety improves most when decisions are optimized—not generalized.

Why This Research Matters Beyond Dams

While focused on dam safety, the implications are broader:

• Infrastructure owners gain economic clarity around safety investments
• Regulators get transparent, defensible risk justification
• Organizations move from compliance-driven controls to ALARP-aligned decision-making

Most importantly, this research shows that quantitative risk frameworks are no longer academic exercises—they are operationally achievable today.

A Note of Appreciation

We’re proud that Riskion played a central role in enabling this research and grateful to Tareq Salloum for applying the platform with such rigor and transparency.

Read the full paper here:
https://www.researchgate.net/publication/399667482_A_Risk-Informed_Framework_for_Public_Safety_Around_Dams