Screen site-specific liquefaction hazard with Boulanger & Idriss (2014) CPT-based triggering, computed against the site and plotted against published reference boundary curves (Cetin et al., Moss et al., and Idriss & Boulanger (2008)). It draws on 8,634 in-situ test records from the Next Generation Liquefaction (NGL) database.
Input CPT or SPT measurements, shear wave velocity, fines content, depth, groundwater table, and seismic loading (PGA, Mw). The tool accepts the full parameter set used in practice.
The engine searches 8,634 in-situ test records from the NGL database and returns the most geotechnically similar sites, ranked by similarity score. Retrieve the top 1, 3, 5, or 10 matches.
Each result set includes an AI-drafted engineering assessment memo: CSR, CRR, and factor of safety from Boulanger & Idriss (2014), plotted against published reference boundary curves, plus field remarks from the original reconnaissance data. The model computes; the AI drafts and explains.
The engine computes a single, deterministic triggering procedure, Boulanger & Idriss (2014), against every site. Three other published boundary curves are plotted alongside it for visual comparison, not computed.
Semi-empirical CPT- and SPT-based liquefaction triggering procedure, computed deterministically against the site. The only triggering computation in the engine.
Boulanger, R.W. & Idriss, I.M. (2014), “CPT and SPT Based Liquefaction Triggering Procedures,” Report UCD/CGM-14/01, UC Davis.
A 9-point digitized SPT-based boundary curve, pinned at PL=15%, Mw=7.5, σ′v=1 atm, FC=0%, plotted for comparison only.
Cited as Cetin et al. (2004).
A CPT-based triggering boundary curve from the published correlation, plotted for comparison only.
Moss, R.E.S., Seed, R.B., Kayen, R.E., Stewart, J.P., Der Kiureghian, A. & Cetin, K.O. (2006), J. Geotech. Geoenviron. Eng., 132(8).
An earlier CPT- and SPT-based boundary curve from the same authors, plotted for comparison only.
Idriss, I.M. & Boulanger, R.W. (2008), Soil Liquefaction During Earthquakes, EERI Monograph MNO-12.
The published curves do not always agree. Plotting Cetin, Moss, and Idriss & Boulanger alongside the computed Boulanger & Idriss (2014) result shows where the published boundary curves diverge: information for the engineer's judgment, not another computed output. Read the full methodology →
The tool accepts the complete set of parameters used in geotechnical liquefaction assessment.
qc, fs, Fr, Ic, u2
N60, Fines Content, D50, PI, Unit Weight
Vs, Vs30
Depth, GWT Depth, PGA, Mw
Send site parameters, get ranked matches and an AI-drafted assessment back as JSON.
POST /v1/query
Authorization: Bearer <api-key>
Content-Type: application/json
{
"qc": 4.2, // Cone tip resistance (MPa)
"Ic": 1.8, // Soil behaviour type index
"FC": 18, // Fines content (%)
"depth": 6.5, // Sample depth (m)
"PGA": 0.32, // Peak ground acceleration (g)
"Mw": 6.2 // Moment magnitude
}
Billed monthly. Cancel anytime.
$216 billed annually.
Tailored to your organization.
The Liquefaction Hazard Engine computes Boulanger & Idriss (2014) CPT-based triggering, plotted against published reference boundary curves: Cetin et al. (2004); Moss et al. (2006); Idriss & Boulanger (2008). It draws on 8,634 in-situ test records from the NGL database, 354 sites across 31 earthquakes (Brandenberg et al., 2020). Robb Moss, author of Moss et al. (2006), a CPT-based triggering model plotted as a reference curve in the engine, serves on GeoLiquefy's advisory board.
Pro is $30/month billed monthly, or $18/month ($216/year) billed annually. Enterprise pricing is custom, for high-volume usage, priority support, and multi-seat access.
Yes. Submit a JSON payload of site parameters (CPT, SPT, shear wave velocity, site and seismic inputs) to the REST API and receive ranked matches from the NGL database with an AI-drafted assessment in a single response, for GIS and portfolio risk workflows.
Geotechnical consultants, who use it from site investigation to signed report, and reinsurers, cat modelers, and risk analytics teams, who screen liquefaction as a portfolio- level damage multiplier via the API.
The AI does not compute the triggering result. Boulanger & Idriss (2014) computes CSR, CRR, and factor of safety against the matched in-situ test records; the AI drafts the engineering-readable memo and explanation around that computed result.