Generating Noise Inputs for Shake Table Testing

Shake tables are widely used in structural and mechanical engineering research to simulate dynamic loads, including earthquakes, vibrations, and random noise inputs. One common requirement in laboratory testing is generating noise-based input signals to study how structures respond to broadband vibrations. This blog post will guide you through the process of generating noise signals and inputting them into a shake table, with a focus on achieving controlled displacement amplitudes.

Understanding Noise Inputs for Shake Tables

Noise inputs refer to random or controlled vibration signals that can be applied to a test structure using a shake table. Unlike sinusoidal or earthquake simulations, noise-based inputs provide a broad spectrum of frequency content, making them useful for:

  • Structural modal identification
  • Fatigue testing under random loads
  • Simulation of real-world environmental vibrations
  • Testing damping characteristics of structures

Key Considerations When Generating Noise Signals

Before applying a noise signal to a shake table, consider the following factors:

  1. Desired Displacement Amplitude: If you aim to achieve a maximum vibration amplitude (e.g., 1 cm), you must carefully scale your input signal. Displacement is related to acceleration and frequency through fundamental vibration equations.
  2. Frequency Content: White noise provides a flat frequency spectrum, whereas filtered noise can be tailored to a specific range (e.g., low-frequency dominant vibrations).
  3. Shake Table Limits: Ensure that your generated input signal does not exceed the physical displacement, velocity, or acceleration limits of your shake table.

Methods for Generating Noise-Based Inputs

There are multiple approaches to generating noise signals for shake tables:

1. Using MATLAB or Python for Signal Generation

Both MATLAB and Python (with libraries like NumPy and SciPy) can generate noise signals in a format compatible with shake table controllers.

  • MATLAB Example:
fs = 1000; % Sampling frequency in Hz  
t = 0:1/fs:10; % Time vector for 10 seconds  
noise_signal = 0.01 * randn(size(t)); % Generate white noise scaled to desired amplitude  
csvwrite('noise_input.csv', noise_signal); % Save the signal as a CSV file
  • Python Example:
import numpy as np  
import pandas as pd  

fs = 1000  # Sampling frequency in Hz  
t = np.linspace(0, 10, fs*10)  # Time vector for 10 seconds  
noise_signal = 0.01 * np.random.randn(len(t))  # Generate white noise  

# Save the noise signal to a CSV file  
pd.DataFrame(noise_signal).to_csv('noise_input.csv', index=False, header=False)

These signals can then be uploaded to the shake table control software.

2. Using EASYTEST Software for Signal Generation

EASYTEST is the primary software used by most of our shake tables for control and signal processing. It provides a user-friendly interface to generate various types of signals, including:

  • White noise and filtered noise
  • Sine sweep signals for frequency response analysis
  • Custom waveform inputs based on experimental requirements

How to Use EASYTEST for Noise-Based Testing:

  1. Open EASYTEST and navigate to the signal generation module.
  2. Select the “Random Noise” option and configure the amplitude and frequency range.
  3. Specify the duration and sampling rate for the test.
  4. Load the generated signal into the shake table controller and run the test.

3. Sine-Sweep Testing for Structural Identification

Before applying a noise input, it is often helpful to conduct a sine-sweep test to identify the resonance frequencies of the test structure. EASYTEST can also be used to generate sine-sweep signals that gradually increase or decrease in frequency over time. This helps in fine-tuning the noise signal to focus on critical frequency ranges.

Implementing the Noise Input on a Shake Table

Once the noise signal has been generated, follow these steps to apply it to your shake table:

  1. Convert the Signal Format: Ensure the signal is in a format supported by your shake table control system (CSV, TXT, or direct software input).
  2. Scale the Input Properly: If a displacement of 1 cm is required, ensure the noise amplitude is scaled appropriately.
  3. Load the Input into EASYTEST or Shake Table Controller: Import the file and preview the waveform.
  4. Run a Test Simulation: Before running the actual experiment, conduct a short-duration test to verify that the desired displacement is achieved.
  5. Analyze Results: Use accelerometers or displacement sensors to confirm the input and response of the structure.

Conclusion

Generating noise-based inputs for shake table testing is a powerful way to simulate real-world vibration conditions. Whether using MATLAB, Python, or the EASYTEST software, researchers can create controlled random vibration signals tailored to their experimental needs. By understanding the relationship between frequency, displacement, and acceleration, users can ensure precise control over the shake table’s motion.

For users of our shake tables, we highly recommend using EASYTEST for signal generation and control. If you have any questions about generating noise inputs or using EASYTEST, feel free to reach out to us at support@quakelogic.net.

Seeing is Believing!

Ergodic vs. Non-Ergodic Models in Ground Motion Modeling

1. Ergodic Models

An ergodic model assumes that spatial variability in ground motion is equivalent to temporal variability. In other words, it treats the variability of ground motions across different locations as if it represents the variability of ground motions at a single location over time. This assumption allows ground motion prediction equations (GMPEs) to be developed using global datasets from many earthquakes, ignoring site-specific effects.

Key Characteristics of Ergodic Models:
  • Use a large dataset from various regions to develop a generalized ground motion model.
  • Assume that ground motion variability at one site can be inferred from observations at other sites.
  • Do not account for site-specific and path-specific effects, leading to increased uncertainty in ground motion predictions.
  • Overestimate variability at a specific site since they include global variations.
Applications of Ergodic Models:
  • Traditional ground motion prediction equations (GMPEs).
  • Regional seismic hazard assessment.
  • Probabilistic seismic hazard analysis (PSHA) for areas with limited local earthquake data.

2. Non-Ergodic Models

A non-ergodic model does not make the assumption that spatial variability can substitute for temporal variability. Instead, it recognizes that each site and each path between a source and a site has unique, repeatable characteristics that affect ground motion. Non-ergodic models account for site-specific and path-specific effects, reducing uncertainty in seismic hazard analysis.

Key Characteristics of Non-Ergodic Models:
  • Incorporate local geological and geophysical conditions that influence ground motion.
  • Recognize that ground motion at a site is not a random sample from a global dataset but has systematic trends over time.
  • Require region-specific or site-specific datasets for calibration.
  • Reduce aleatory (random) uncertainty and increase epistemic (knowledge-based) uncertainty since the model relies on localized data.
Applications of Non-Ergodic Models:
  • Site-specific seismic hazard analysis for critical infrastructure.
  • Urban seismic hazard mapping, considering localized site effects.
  • Advanced ground motion modeling, incorporating physics-based simulations and machine learning to refine predictions.

Why Use Non-Ergodic Models?

Traditional ergodic models overestimate variability at a specific site because they include data from many locations, leading to conservative hazard estimates. In contrast, non-ergodic models provide more accurate site-specific predictions by incorporating long-term local seismic behavior, reducing uncertainty.

However, non-ergodic models require significant local data to be properly constrained, which can be a challenge in regions with limited seismic monitoring.

Summary Table:

FeatureErgodic ModelNon-Ergodic Model
AssumptionSpatial variability represents temporal variabilityRecognizes site-specific and path-specific effects
Data UseLarge dataset from various locationsSite-specific or path-specific data
UncertaintyHigher aleatory uncertaintyReduced aleatory, higher epistemic uncertainty
ApplicationRegional seismic hazard analysis, GMPEsSite-specific hazard analysis, infrastructure design
AdvantageWorks with limited local dataMore accurate ground motion predictions

In recent years, there has been a shift towards non-ergodic models for site-specific seismic hazard assessment, particularly for critical infrastructure projects. Advances in machine learning, physics-based simulations, and high-resolution seismic data have made non-ergodic models more viable for practical applications.

Acoustic Emission Monitoring for Detecting Cracks in Steel Bridges

The safety and longevity of steel bridges are vital for transportation infrastructure. Continuous exposure to traffic-induced vibrations, thermal fluctuations, and environmental stresses can lead to structural degradation over time. Acoustic Emission Monitoring (AEM) provides a real-time, advanced approach to detecting and tracking crack propagation in steel bridges, enabling early maintenance and extending service life.

Æmission Digitizer/Recorder: The Core of Our AEM System

At the heart of our monitoring solution is Æmission, a state-of-the-art acoustic emission monitoring system designed for high-speed data acquisition and real-time signal processing.

  • High-Speed Data Acquisition: Operates at 1.25 MSps @ 18-bit resolution or 5 MSps @ 16-bit resolution, ensuring high-fidelity signal capture.
  • Patented FPGA Algorithms: Developed in collaboration with the Polytechnic University of Turin, enabling onboard processing of acoustic emission waves.
  • Localized Data Processing: Extracts key crack progression indicators, such as βt, b-value, and cumulative count, facilitating predictive maintenance strategies.
  • Proven Performance: Validated through the MONFRON project, a large-scale experimental initiative funded by Regione Toscana in Italy.

Acoustic Emission (AE) Technology for Structural Health Monitoring

Acoustic emission (AE) is the release of stress waves within a material caused by internal structural changes or external mechanical loads. These waves propagate through the material and can be detected to assess its condition, revealing cracks or other forms of damage.

AE testing is a non-destructive technique used to identify and monitor crack development in structures, including metals, concrete, and composites. When subjected to mechanical stress, temperature variations, or environmental changes, a structure generates acoustic emissions that sensors capture on its surface.

The recorded signals are processed using advanced software and hardware to pinpoint the AE source and locate potential damage. Continuous monitoring allows engineers to track crack progression, evaluate structural integrity, and make data-driven decisions regarding maintenance, repairs, or replacements. AE testing is a crucial tool for ensuring the safety and longevity of critical structures across industries such as aerospace, civil engineering, and manufacturing.

Application of AEM in Steel Bridges

Steel bridges experience constant mechanical and environmental stress, making them susceptible to fatigue cracks and localized failures. Implementing an AEM system on existing steel bridges provides real-time insights into structural integrity and ensures early intervention before catastrophic failures occur.

Use Cases:

  • Traffic-Induced Vibrations: AE sensors monitor crack initiation and progression in high-stress zones such as welds and riveted connections.
  • Thermal Fluctuations: Seasonal temperature changes cause expansion and contraction, exacerbating material fatigue.
  • Corrosion Monitoring: Detects stress-corrosion cracking, an insidious form of material degradation.
  • Emergency Event Detection: Sudden impacts (e.g., vehicle collisions, seismic activity) introduce immediate damage, with AE-based monitoring aiding rapid response.
  • Predictive Maintenance Planning: Engineers analyze AE data trends to forecast maintenance needs, minimizing costs and avoiding unscheduled repairs.

Æmission System Architecture

Æmission is supplied with eight piezoceramic sensors, selected and characterized with assistance from the Polytechnic University of Turin for optimal civil structure monitoring. These sensors are strategically placed around the monitored area and connected via 10-meter cables.

Key Features:

  • Analog Signal Processing: The analog signals from the piezoceramic sensors are conditioned and level-adapted by a cascade of analog filters before digital conversion.
  • High-Speed Data Conversion: Eight high-speed ADCs (1.25MSps@18bit or 5MSps@16bit) continuously convert analog signals into digital format, synchronized to the same clock source.
  • Parallel Processing with FPGA: Digital signals are acquired and processed in parallel by the FPGA, with only relevant events transferred to the Linux CPU.
  • Data Storage & Remote Sharing: Events are stored locally within the Linux CPU and can be shared remotely via WiFi or 3.5G connection.
  • Integrated GNSS Receiver: Synchronizes multiple Æmission units, enabling scalable monitoring across extensive infrastructures.
  • Comprehensive Data Analysis: After sufficient monitoring, parameter plots help analyze cracking patterns and structural health trends.

Real-World Monitoring Example

The following graphs represent an ongoing acoustic emission survey in a marble quarry:

  • AE Cumulative Count
  • AE/hour Trends
  • Event Frequency Distribution
  • Amplitude Variations
  • βt and b-value Progression
  • 3D Localization of Emission Sources

In the 3D representation, blue squares denote AE sensors, while red dots indicate the localization of emission sources.

Implementation Plan

Our proposal outlines a comprehensive approach to designing, installing, and maintaining an AEM system for steel bridges:

  1. Site Assessment & Sensor Placement: Identify high-risk zones and strategically install AE sensors.
  2. Real-Time Data Collection & Processing: Utilize the Æmission digitizer/recorder for continuous monitoring.
  3. Data Interpretation & Reporting: Implement advanced algorithms to analyze AE parameters and generate actionable insights.
  4. Predictive Maintenance & Intervention: Leverage AEM data to schedule repairs before structural failure occurs.

Why QuakeLogic’s AE Monitoring System?

QuakeLogic’s AE monitoring system is a cutting-edge solution for steel bridge health assessment. Our system is designed for high-performance data acquisition, real-time crack detection, and predictive maintenance planning. By investing in our AE monitoring technology, bridge owners and engineers can ensure structural safety, extend service life, and reduce maintenance costs.

Buy Our AE System Today!

Visit our website to explore our state-of-the-art acoustic emission monitoring hardware and equip your infrastructure with the latest technology for proactive maintenance.

BUY ACOUSTIC EMISSION SYSTEM NOW!

About QuakeLogic

QuakeLogic is a global leader in monitoring solutions, offering innovative technologies for accurate seismic data acquisition and analysis. Our solutions empower organizations worldwide to predict, understand, and mitigate risks effectively.

For more information or inquiries, reach out to our sales team today!