Rugg Geotechnics

08/22/2026

Critical state soil mechanics and NorSand can be difficult to understand intuitively from equations and static figures alone.

The state parameter ψ, contraction and dilation, critical-state calibration, and the resulting stress-strain response are closely connected, but it can be challenging to visualize how they work together.

To make these concepts easier to explore, I developed an interactive companion to Jefferies and Been’s Soil Liquefaction: A Critical State Approach.

Module 1 covers the critical-state framework, the state parameter ψ, stress-dilatancy, laboratory calibration, and NorSand.

The module is free, runs directly in a browser, and requires no sign-up. You can:
• Move a soil state relative to the critical state line and see ψ update in real time
• Explore why the same relative density can produce different behavior at different stress levels
• Investigate how ψ influences contraction, dilation, peak stress ratio, and friction angle
• Work through the calibration of critical-state properties using published values for 17 sands and tailings
• Run a NorSand stress-strain simulation and examine the influence of each model parameter
• Test your understanding with a 100-question quiz divided into short, topic-specific checkpoints

The goal is not to replace the textbook. It is to provide a visual and interactive environment for developing the intuition needed to understand the equations and apply them more confidently.

If you are a geotechnical engineering student, a practicing engineer revisiting critical-state concepts, or simply curious about the mechanics behind liquefaction assessment, I hope you find it useful.

🔗 Explore the interactive module:
https://lnkd.in/gwfRZjkh

There is also a comment section at the end. If something is unclear, you identify an error, or you have suggestions for improving the learning experience, I would genuinely appreciate your feedback.

Three more interactive modules are on the way, extending the learning path from critical-state fundamentals to field assessment and liquefaction analysis.

This link will take you to a page that’s not on LinkedIn

07/13/2026

لە ئاوێزەی وەڵامی بوومەلەرزە تێ بگە

07/09/2026

Many people think PGA and response spectrum are the same — but they are not.

PGA means Peak Ground Acceleration.
It tells us the maximum acceleration of the ground during an earthquake.

Response spectrum shows how a structure responds to that ground motion.

Different structures respond differently because they have different natural periods and damping.

Simple example:

Ground acceleration = earthquake input
Response spectrum = structural response output

So in seismic design, engineers do not only check PGA. They also check spectral acceleration at the important periods of the structure.

Easy way to remember:

PGA tells you how hard the ground shakes.
Response spectra tell you how hard the structure responds.

Follow Civil Engineer & Researcher / CER for more simple engineering explanations.

06/30/2026

🪨💧 Excited to share our latest research!

We're proud to share our new paper, published in the International Journal of Geotechnical Engineering, tackling a real challenge in mine tailings management: predicting how tailings behave as they dry out and consolidate over time.

Tailings (the leftover material from mining) are typically stored in large facilities behind earthen dams, and understanding their water movement and volume change is critical for keeping these structures safe. The problem? Some of the key properties needed for accurate predictions, like hydraulic conductivity, are notoriously hard to measure directly.

In our paper, we developed a novel inverse model — a numerical optimization tool that works backward from sensor data embedded in the tailings to back-calculate these hard-to-get parameters. We tested it against published lab data on phosphate tailings and polymer-treated fine tailings, and the model successfully reproduced their behavior with strong accuracy.

🔍 Why it matters: This approach gives engineers a practical new tool for understanding tailings behavior without relying solely on extensive lab or field testing, supporting safer facility design and stronger environmental risk management in the mining industry.

📄 Check it out here: https://www.tandfonline.com/doi/abs/10.1080/19386362.2024.2435447

This paper presents a novel nonlinear least-squares numerical optimization scheme, inverse model, which was used to derive hydraulic conductivity parameters via sensors embedded in tailings with kn...