Inputs
Detailed display
Maintains detailed display state in shared URL.
Results
These results are for learning only and are not intended to determine the actual level of risk.
| Item | value |
|---|---|
| Shake coefficient F_shake | — |
| Groundwater coefficient F_gw | — |
| Soil coefficient F_soil | — |
| PGA Conversion (gal) | — |
score = 100 × F_shake × F_gw × F_soil
Sensitivity graph (groundwater level)
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Explanation (for learning)
- The stronger the shaking, the more likely liquefaction will occur.
- The shallower the groundwater level, the more likely liquefaction will occur.
- The looser the sand, the more likely it is that liquefaction will occur.
Practical evaluation requires N value, CPT, particle size distribution, layer structure, seasonal variation of groundwater level, seismic motion evaluation, etc.
How to use this calculator effectively
This guide helps you use Simple indicator of liquefaction (for educational use) in a repeatable way: define a baseline, change one variable at a time, and interpret outputs with explicit assumptions before you share or act on results.
How it works
The page applies deterministic logic to your inputs and shows rounded output for readability. Treat it as a comparison workflow: run one baseline case, adjust a single parameter, and measure both absolute and percentage deltas. If a result seems off, verify units, time basis, and sign conventions before drawing conclusions. This approach keeps your analysis reproducible across teammates and sessions.
When to use
Use this page when you need a fast estimate, a classroom check, or a practical what-if comparison. It works best for planning and prioritization steps where you need direction and magnitude quickly before investing in deeper modeling, manual spreadsheets, or formal external review.
Common mistakes to avoid
- Changing multiple parameters at once, which hides the true cause of output movement.
- Mixing units (percent vs decimal, monthly vs yearly, gross vs net) across scenarios.
- Comparing with another tool without aligning defaults, constants, and rounding rules.
- Using rounded display values as exact downstream inputs without re-checking precision.
Interpretation and worked example
Run a baseline scenario and keep that result visible. Next, modify one assumption to reflect your realistic alternative and compare direction plus size of change. If the direction matches your domain expectation and the size is plausible, your setup is usually coherent. If not, check hidden defaults, boundary conditions, and interpretation notes before deciding which scenario to adopt.
See also
FAQ
What is liquefaction?
This is a phenomenon in which sandy ground temporarily loses its strength due to earthquake shaking, causing subsidence and sand blowing.
What does this tool calculate?
A learning coefficient is applied to the strength of shaking, groundwater level, and ground category, and a simple score from 0 to 100 is displayed.
Can it be used in practice?
Cannot be used. It is not a substitute for practical judgment involving ground investigation and professional evaluation.
Is a higher score more dangerous?
This is a guideline for learning. It is not intended to determine the actual level of risk.
What should I do first on this page?
Start with the minimum required inputs or the first action shown near the primary button. Keep optional settings at defaults for a baseline run, then change one setting at a time so you can explain what caused each output change.
How to use Simple indicator of liquefaction (for educational use) effectively
What this calculator does
This page is for estimating outcomes by changing inputs in one controlled workflow. The model keeps your focus on variables, not output shape. Start with stable assumptions, then test sensitivity by changing one key input at a time to observe directional impact.
Input meaning and unit policy
Each input has an expected unit and a typical range. For reliable interpretation, check whether you are using the same unit system, period, and base assumptions across all runs. Unit mismatch is the most common source of unexpected drift in numeric results.
Use-case sequence
A practical sequence is: first run with defaults, then create a baseline log, then run one alternative scenario, and finally compare only the changed output metric. This sequence reduces cognitive load and prevents false pattern recognition in early experiments.
Common mistakes to avoid
Avoid changing too many variables at once, mixing incompatible data sources, and interpreting a one-time output without checking robustness. A single contradictory input can flip conclusions, so keep each experiment minimal and document assumptions as part of your note.
Interpretation guidance
Review both magnitude and direction. Direction tells you whether a strategy moves outcomes in the desired direction, while magnitude helps you judge practicality. If both agree, you can proceed; if not, rebuild the baseline and verify constraints before deciding.
Operational checkpoint 1
Record the exact values and intent before you finalize any comparison. Confirm the unit system, date context, and business constraints. Compare outputs side by side and check whether differences are explained by one changed variable or by hidden assumptions. This checkpoint often reveals the single factor that changed everything.
Related tools
- Geology/Environment (Disaster prevention (earthquake/tsunami/ground))
- P wave/S wave: Initial tremor duration → epicenter distance/occurrence time (for education) (ES-007)
- Moment magnitude Mw (Fault → Mo → Mw) (ES-015)
- Earthquake catalog → Gutenberg–Richter (b-value estimation) (ES-024)
- Tsunami wave speed/arrival time (shallow water wave c=√(gh)) (ES-008)