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    Bridging the Model–Reality Gap with Site Monitoring Data Optimization of an Excavation Model using the Inclinometer Readings

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    AI Summary

     This session explores how excavation models can be optimized using inclinometer readings and site monitoring data to reduce the gap between analysis results and real field behavior. Through a 2D excavation retaining structure model in MIDAS GTS NX, the session explains how uncertainties such as soil properties, modelling assumptions, soil–structure interface conditions, groundwater, and unexpected soft layers can affect excavation predictions. It also introduces surrogate modelling-based back analysis as a practical method to calibrate soil parameters, improve model reliability, and make forward prediction more efficient. Participants will gain practical guidance on using monitoring data, back analysis, correlation analysis, and surrogate models to support smarter and more reliable geotechnical design. 

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    Description

    In this session, we will present a practical workflow for optimizing an excavation model using inclinometer readings and site monitoring data. The session begins with the common challenge in excavation design: analysis results may differ from actual site measurements due to soil uncertainty, modelling assumptions, groundwater conditions, soil–structure interaction, or unexpected ground layers.

     

    The session will then demonstrate how back analysis can be used to calibrate model parameters and reduce the gap between predicted and measured displacement. Attendees will learn how surrogate modelling, design variables, sensor data input, correlation analysis, and response surface-based optimization can be applied in MIDAS GTS NX to improve model reliability and support more confident prediction for the next construction stage.

    Key Points

    Model vs. Reality Gap in Excavation AnalysisUnderstand why excavation analysis results may differ from actual site measurements due to soil uncertainty, modelling assumptions, groundwater conditions, and unexpected ground layers.

    Back Analysis using Site Monitoring DataLearn how inclinometer readings and field monitoring data can be used to calibrate soil parameters and improve the reliability of FEM excavation models.

    Surrogate Modelling for Efficient OptimizationExplore how surrogate modelling can reduce repeated FEM analysis time by creating a fast mathematical approximation for model calibration and optimization.

    Correlation Analysis and Key Parameter IdentificationSee how correlation analysis helps identify which design variables have the greatest influence on model accuracy, allowing engineers to focus on the most important soil parameters.

    Speaker

    Harsha Tadavarthi Geotechnical Team Leader & Technical Manager MIDAS IT Australia
    Harsha Tadavarthi is the Technical Manager for the Oceania region at MIDAS IT, with over 8 years of experience in geotechnical engineering. He specializes in advanced numerical modeling and provides expert support for geotechnical and structural analysis across dams, hydropower, underground, mining, and other critical infrastructure projects. Harsha's expertise also includes thermal and seismic simulation of dams, as well as the design and analysis of complex geotechnical systems. He leads the seamless delivery of solutions in dam engineering, ensuring industry-leading support and innovation in geotechnical modeling. Through consultancy, training, and international workshops, he empowers professionals with both practical insights and theoretical foundations in geotechnical and structural modeling, fostering excellence across the engineering community.
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