ASCE 7-22 VS30 Site Classification Requirements: DoReMi® MASW Turnkey Seismograph + SEISMOWIN Solution

Streamline ASCE 7-22 VS30 Site Classification Using DoReMi® MASW Seismographs with SEISMOWIN

The latest ASCE 7-22 standard requires Vs30 shear wave velocity measurements as a key parameter for site classification and seismic design. This shift makes reliable, efficient, and accurate seismic characterization of soils more critical than ever for geotechnical engineers, structural designers, and regulatory compliance.

The DoReMi® Seismograph is the perfect solution. Its digital telemetry system with embedded electronics offers unmatched efficiency, scalability, and modularity. Each channel operates as an independent seismograph, enabling networks from 1 to 255 channels for projects of any scale. Lightweight, durable, and powered by a rechargeable battery with smart standby mode, DoReMi® ensures seamless field deployment and uninterrupted operation. With over 15 years of proven reliability and hundreds of global clients, it is a trusted standard in seismic surveys and geotechnical site investigations.

Paired with SEISMOWIN, a complete seismic data management and analysis suite, the DoReMi® becomes a turnkey solution for Vs30 determination. SEISMOWIN’s MASW (Multichannel Analysis of Surface Waves) and ReMi (Refraction Microtremor) modules provide advanced tools to analyze both active-source surface waves and ambient noise, ensuring flexibility in diverse field conditions. MASW delivers detailed S-wave velocity profiles comparable to borehole logs—without being affected by velocity reversals—while ReMi excels in noisy environments where passive surveys are preferable.

Together, DoReMi® + SEISMOWIN empower engineers to quickly and confidently obtain Vs30 values required under ASCE 7-22, streamlining the entire workflow from data acquisition to final reporting. In-house design, production, and support guarantee fast service, expert training, and customization to meet project-specific needs.

Turnkey Package Includes:

  • DoReMi® Seismograph with 12 or 24+ channels and 4.5 Hz geophones
  • SEISMOWIN software with MASW & ReMi modules
  • Complete field-ready system with battery, cabling, and accessories
  • Training and technical support

With this integrated solution, compliance with ASCE 7-22 site classification requirements is no longer a challenge—it’s an opportunity to deliver faster, more reliable, and cost-effective results.

DoReMi Seismograph: The All-in-One Solution

Key Features of DoReMi Seismograph:

  • Modular Design: Scalable to support 1 to 255 channels, allowing flexible configurations for diverse projects.
  • Embedded Recording Electronics: Electronics are embedded in the cable, reducing electromagnetic interference.
  • Lightweight & Portable: Easily transported with a cable wheeler, ensuring smooth deployment in remote sites.
  • Integrated Battery System: Built-in rechargeable battery ensures continuous and independent operation.
  • Noise Reduction: Digitalization near the geophone minimizes noise and prevents data loss or crosstalk.
  • Flexible Sensor Integration: Supports 4.5 Hz geophones, downhole sensors (SS-BH-5C), and other seismic equipment.
  • Free Analysis Software: Compatible with any processing software, simplifying data management and interpretation.
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Advanced Software for Seamless Operation

The DoReMi Seismograph is complemented by advanced software tools, designed to streamline on-site data quality checks and post-processing workflows.

Key Software Capabilities:

  • Pre-Shot Noise Monitoring: Ensures data integrity before acquisition.
  • Downhole & Surface Data Management: Simplifies different acquisition scenarios.
  • Signal Inversion & Overlapping: For SH shots and advanced processing.
  • Data Filtering & Spectral Analysis: Advanced tools for FK and FV analysis.
  • Roll-Along Acquisition: Simplifies large-area surveys.
  • HVSR Preview: Horizontal-to-Vertical Spectral Ratio preview for subsurface mapping.
  • Multi-Language Support: Available in English, Italian, and Chinese.

Applications of DoReMi Seismograph

  • Seismic Hazard Assessment: Earthquake resilience site characterization.
  • Geophysical Exploration: MASW, ReMi, Refraction, Reflection, and Downhole surveys.
  • Infrastructure Projects: Foundation analysis and underground mapping.
  • Resource Exploration: Aquifer detection, oil and gas reservoir profiling.
  • Urban Development: Roadbed evaluations and soil stiffness assessments.

Data Outputs from DoReMi Seismograph

  1. 1D Shear Wave Velocity Profile:
    • Vertical shear-wave velocity analysis for site characterization.
  2. 2D Shear Wave Velocity Profile:
    • Comprehensive subsurface mapping when multiple acquisitions are performed.

These outputs are essential for geotechnical engineers, seismologists, and urban planners in making informed decisions.

Why Choose DoReMi Seismograph for MASW and ReMi Surveys?

  • Dual Capability: Seamlessly supports both MASW and ReMi techniques.
  • High Precision: Noise-free, reliable data acquisition.
  • Scalable Design: Flexible configurations from 1 to 255 channels.
  • Advanced Software Integration: Simplified analysis and data management.
  • Portability: Lightweight design with modular architecture.
  • Expert Support: Dedicated training, support, and consultation from QuakeLogic.

Conclusion

The DoReMi Seismograph by QuakeLogic represents a state-of-the-art solution for MASW and ReMi seismic surveys, offering unmatched flexibility, precision, and reliability. Whether it’s mapping shallow shear-wave velocity using MASW or profiling deeper subsurface layers with ReMi, DoReMi delivers results you can trust.

Experience precision, reliability, and innovation with the DoReMi Seismograph—your trusted partner in seismic exploration.

📞 For more information or to request a demo, contact us at:
Phone: +1-916-899-0391
Email: sales@quakelogic.net
Website: https://products.quakelogic.net/product/doremi-seismographs/


QUAKEMATE: Bringing Earthquake Science to Classrooms

Affordable Shake Table for K-12 & Universities

At QuakeLogic, we believe that hands-on learning is the most powerful way to inspire the next generation of scientists and engineers. That’s why we developed QUAKEMATE, a small-scale, classroom-ready shake table designed to make earthquake science engaging, practical, and affordable.


Why QUAKEMATE?

Earthquakes are powerful reminders of nature’s force, and understanding them is vital for building safer communities. QUAKEMATE gives students the opportunity to experience realistic seismic simulations right inside their classroom or lab — no advanced equipment or technical setup required.

With QUAKEMATE, students can:

  • Test Model Structures: Build and shake bridges, towers, and houses to see how they react.
  • Learn Resonant Frequencies: Discover why some structures collapse while others survive.
  • Explore Engineering Concepts: Apply physics and design principles to strengthen their models.
  • Engage in Teamwork: Collaborate on exciting experiments that bring theory to life.

Key Features

  • Realistic Simulation – Replicates seismic wave patterns to mimic earthquake behavior.
  • Advanced LED Control – Adjustable cycles (0–30 Hz) to match real-world P-wave frequencies.
  • Custom Sequences – Program up to 8 minutes of unique shaking patterns.
  • Classroom-Friendly Design – Lightweight, quiet, and safe for students of all ages.
  • Durable Build – Built for long-term educational use at an accessible price.
  • Hands-On STEM Learning – Includes plywood plates, bolts, and washers for simulating loads.

Specifications at a Glance

  • Power: 110V & 220V compatible
  • Payload: Up to 30 kg
  • Operation: Standalone (no computer needed)
  • Control: LED display, programmable sequences
  • Extras: Comes with setup guide and student project ideas

A Powerful Educational Tool

QUAKEMATE isn’t just a lab device — it’s an educational experience. From elementary schools to engineering programs, this shake table helps students connect theory with practice, making lessons in physics, geology, engineering, and resilience come alive.

Imagine a classroom where students build miniature skyscrapers, program a quake sequence, and then watch how their designs perform under simulated seismic stress. With QUAKEMATE, seeing is believing.


Frequently Asked Questions

Q: Is QUAKEMATE safe for classrooms?
Yes — it’s designed for safe, risk-free use in K-12 and university environments.

Q: What kind of structures can be tested?
From popsicle-stick bridges to LEGO® towers, any small-scale model can be tested.

Q: Does it replicate real earthquakes?
It mimics seismic motion patterns, helping students understand how structures respond.

Q: Can students program their own shake patterns?
Absolutely — up to 8 minutes of custom shaking can be set.


Who Is QUAKEMATE For?

  • K-12 Schools – Hands-on STEM learning for science fairs, labs, and afterschool programs.
  • Universities – Introductory tool for civil engineering, physics, and seismology courses.
  • STEM Outreach Programs – Demonstrations for public education and disaster preparedness.

Conclusion

The QUAKEMATE Shake Table is an affordable, portable, and powerful tool for making earthquake education exciting and interactive. It bridges the gap between classroom theory and real-world science, empowering students to become future engineers, innovators, and problem-solvers.

👉 Ready to bring QUAKEMATE to your classroom or lab?
📞 Call us at +1-916-899-0391 | 📧 Email: sales@quakelogic.net
🌐 Visit us at https://products.quakelogic.net/product/eqs-tremor-table/

Admittance Control: Concept, Applications, and Insights

Admittance control is a fundamental control strategy in robotics and mechatronics that governs how a system interacts with its environment. It is designed to make a system respond to external forces by producing a corresponding motion, such as a change in velocity or position, based on a predefined dynamic relationship. This compliance-oriented approach stands in contrast to impedance control, where the system generates a force in response to an imposed motion. Admittance control’s ability to yield to external forces makes it particularly valuable in applications requiring adaptability and safety, such as human-robot collaboration, industrial assembly, and haptic interfaces.

Understanding Admittance Control

At its core, admittance control defines how a system moves in response to an applied force. It is often implemented through a two-loop control structure. The outer loop measures the interaction forces—typically using force or torque sensors—and calculates the desired motion based on a specified admittance model. This model incorporates virtual parameters like mass, damping, and stiffness to shape the system’s dynamic response.

Once the desired motion is determined, the inner loop ensures the system accurately follows the computed trajectory using position or velocity control. This force-to-motion approach is especially suited for robots with precise motion control, allowing them to adjust smoothly to external forces rather than trying to generate counteracting forces directly.

The Admittance control can be split into 3 stages. Outer loop (for measuring the external force/torque), calculation of the admittance model and the inner loop. Let’s dive into each stages hereunder.

1. Force/Torque Measurement (Outer Loop)

For the outer loop there are 2 methods that could be used.

a) Current Estimation:

Current estimation is the process of determining the actual electric current flowing through a system, either by direct measurement or mathematical models. It is commonly used in motor control, battery management, and power electronics to monitor and control current without expensive sensors. By using voltage readings and system models, current can be accurately estimated even without direct measurement.

b) Using a force/torque sensor:

force/torque sensor mounted on the robot’s end-effector or relevant joint continuously measures the forces and torques arising from interaction with the environment. These readings can directly be fed into the outer loop of the control system.

For example, Acrome provides a force/torque sensor option for its Stewart Platform products, as can be seen in the image below. Having a direct sensor measurement simplifies the calculations of the force/torque set points.

Acrome Stewart Platform with a 6D Force-Torque Sensor

2. Calculation of the Admittance Model

The measured force/torque data is input into a predefined admittance model (e.g., Mx¨+Dx˙+Kx=F), where: 

  • M: virtual mass (inertia),
  • D: damping coefficient,
  • K: stiffness coefficient,
  • F: external force,
  • x: position (motion)

The output of this model determines how the system should move, typically in terms of velocity or position.

3. Inner Loop – Motion Execution

In the inner control loop, the robot’s actuators use position or velocity controllers to follow the calculated motion. Instead of counteracting the external force directly, the robot complies with it and adjusts its movement accordingly.

The experimental setup and visual feedback provided to the subjects during the experiments [1]

Applications of Admittance Control

Industrial Robotics

In manufacturing and assembly, robots often need to interact with objects and surfaces in a flexible yet precise manner. Admittance control allows robots to adapt their movement based on physical contact, reducing the risk of jamming or misalignment and improving the efficiency of automated processes.

Human-Robot Interaction in Tesla’s Optimus

In collaborative environments, safety and adaptability are essential. Tesla’s humanoid robot, Optimus, embodies these principles by integrating advanced AI and real-time sensor feedback to interact safely and intuitively with humans. Drawing from Tesla’s Full Self-Driving (FSD) technology, Optimus can perceive its surroundings, predict human motion, and respond accordingly.

One of the key elements in making human-robot interaction seamless is admittance control—a feature Tesla is expected to incorporate into Optimus. This control method allows the robot to sense and react to external forces applied by humans, enabling it to yield or adjust its motion dynamically. For instance, if a human gently pushes Optimus aside while passing through a narrow space, the robot can safely and compliantly give way without resistance or loss of balance.

This kind of responsive behavior is critical in environments where robots and humans share tasks—such as in homes, factories, or healthcare settings. By continuously adjusting its posture and actions based on physical feedback, Optimus minimizes the risk of injury and promotes

trust and collaboration. Tesla’s focus on combining AI perception, motion planning, and human-safe control mechanisms positions Optimus as a powerful example of the future of human-robot collaboration.

Tesla Optimus Robot [2]

Haptic Interfaces

In virtual reality and teleoperation systems, admittance control helps create realistic force feedback. For instance, when using a haptic device, a user might feel the sensation of touching a virtual wall or holding an object. By translating applied forces into controlled movements, admittance control makes digital interactions feel more natural and immersive.

Rehabilitation Robotics

Rehabilitation robots use admittance control to assist patients in physical therapy by adjusting the level of support based on the patient’s movements. This ensures that assistance is provided only when necessary, encouraging active participation and aiding in the recovery process.

Legged Robotics

In legged robots, admittance control helps adjust how the legs respond to different terrains, allowing robots to walk more naturally on uneven surfaces. This improves stability and adaptability in dynamic environments, making it valuable for applications like search-and-rescue or exploration.

Advantages and Challenges

Admittance control offers several benefits, making it a widely used approach. It allows for better interaction with rigid environments, preventing excessive forces that could cause damage [3]. It is also relatively easy to implement on systems with strong motion control capabilities, and the parameters can be adjusted to fine-tune the interaction dynamics.

However, there are also challenges. The approach relies heavily on accurate force sensing, which can be costly and prone to noise, affecting system performance [3]. Stability is another concern—if the system does not respond quickly enough, it can lead to oscillations or instability. To address these limitations, some systems combine admittance control with impedance control, leveraging the strengths of both approaches.

Challenges Due to Orientation-Dependent Force/Torque Sensor Readings in Admittance Control

In admittance control architectures, Force/Torque (F/T) sensors play a crucial role in detecting the external forces applied by the human or the environment. However, these sensors can introduce significant challenges, especially due to their sensitivity to changes in orientation. Since F/T sensors measure forces in their local coordinate frame, any change in the orientation of the robot end-effector may result in a shift of the perceived direction and magnitude of the applied forces. This issue becomes particularly problematic when the center of mass of the attached tool is not aligned with the sensor’s coordinate system, causing gravity-induced forces to project differently depending on the tool’s orientation.

Such effects may lead to misleading force readings, where the sensor interprets gravitational components as user-applied forces. For example, during a drilling task, as the orientation of the robot arm changes, the weight of the drill may create additional force components in unintended axes, potentially degrading the control performance. As highlighted in [4], filtering the raw force measurements and accounting for orientation-dependent effects are essential for stable and transparent human-robot interaction. Proper compensation or transformation of sensor data is therefore necessary to ensure that the control system accurately interprets external inputs and maintains safe and intuitive behavior​. 

Conclusion

Admittance control is a powerful and flexible method that enhances how robots interact with their environment. Whether in manufacturing, healthcare, or human-robot collaboration, its ability to adapt to external forces makes it a critical tool in modern robotics. While challenges like force sensing and stability remain, continuous advancements are refining its implementation, ensuring its continued relevance in future robotic applications. By blending precision with adaptability, admittance control plays a key role in shaping the next generation of interactive robotic systems.

Resources:

[1] Y. Aydin, O. Tokatli, V. Patoglu, and C. Basdogan, “Stable Physical Human-Robot Interaction Using Fractional Order Admittance Control,” in IEEE Transactions on Haptics, vol. 11, no. 3, pp. 464-475, 1 July-Sept. 2018, doi: 10.1109/TOH.2018.2810871.

[2] “Optimus (robot),” Wikipedia: The Free Encyclopedia, https://en.wikipedia.org/wiki/Optimus_(robot) (accessed Apr. 20, 2025).

[3] A. Q. Keemink, H. van der Kooij, and A. H. Stienen, “Admittance control for physical human–robot interaction,” The International Journal of Robotics Research, vol. 37, no. 11, pp. 1421–1444, Sep. 2018, doi: 10.1177/0278364918768950.

[4] A. Madani, P. P. Niaz, B. Guler, Y. Aydin and C. Basdogan, “Robot-Assisted Drilling on Curved Surfaces with Haptic Guidance under Adaptive Admittance Control,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 3723-3730, doi: 10.1109/IROS47612.2022.9982000. 

[5] D. Sirintuna, Y. Aydin, O. Caldiran, O. Tokatli, V. Patoglu, and C. Basdogan, “A Variable-Fractional Order Admittance Controller for pHRI,” IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 10162-10168, doi: 10.1109/ICRA40945.2020.9197288.

[6] Y. Sun, M. Van, S. McIlvanna, N. N. Minh, S. McLoone, and D. Ceglarek, “Adaptive admittance control for safety-critical physical human-robot collaboration,” *IFAC-PapersOnLine*, vol. 56, no. 2, pp. 1313-1318, 2023, doi: https://doi.org/10.1016/j.ifacol.2023.10.1772. 

[7] C. T. Landi, F. Ferraguti, L. Sabattini, C. Secchi, and C. Fantuzzi, “Admittance control parameter adaptation for physical human-robot interaction,”IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 2911-2916, doi: 10.1109/ICRA.2017.7989338. 

[8] H. Zhan,  D. Ye, C. Zeng, and C. Yang, “Hybrid variable admittance force tracking and fixed-time position control for robot–environment interaction,” Robotic Intelligence and Automation, vol. 45, no. 1, pp. 1-12, 2025. doi: 

[9] ARISE Project, “Advanced AI and robotics for autonomous task performance,” Horizon Europe Project 101135959, [Online]. Available: https://cordis.europa.eu/project/id/101135959

[10] Y. Aydin, O. Tokatli, V. Patoglu and C. Basdogan, “A Computational Multicriteria Optimization Approach to Controller Design for Physical Human-Robot Interaction,” in IEEE Transactions on Robotics, vol. 36, no. 6, pp. 1791-1804, Dec. 2020, doi: 10.1109/TRO.2020.2998606.

[11] A. Madani, P. P. Niaz, B. Guler, Y. Aydin and C. Basdogan, “Robot-Assisted Drilling on Curved Surfaces with Haptic Guidance under Adaptive Admittance Control,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 3723-3730, doi: 10.1109/IROS47612.2022.9982000. 

[12] Y. M. Hamad, Y. Aydin and C. Basdogan, “Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction,” in IEEE Transactions on Haptics, vol. 14, no. 4, pp. 750-761, 1 Oct.-Dec. 2021, doi: 10.1109/TOH.2021.3071626.

[13] B. Guler, P. P. Niaz, A. Madani, Y. Aydin, C. Basdogan,

“An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning,” in Mechatronics, vol. 86, 102851, 2022, doi: https://doi.org/10.1016/j.mechatronics.2022.102851.

[14] F. Dimeas and N. Aspragathos, “Online stability in human-robot cooperation with admittance control,” IEEE Transactions on Haptics, vol. 9, no. 2, pp. 267–278, Apr./Jun. 2016.

[15] J. E. Colgate and N. Hogan, “Robust control of dynamically interacting systems,” International Journal of Control, vol. 48, no. 1, pp.  65–88, 1988.

[16] S. P. Buerger and N. Hogan, “Complementary stability and loop shaping for improved human–robot interaction,” IEEE Transactions on Robotics, vol. 23, no. 2, pp. 232–244, Apr. 2007.