skip to main content

Title: Physics-Informed Data-Driven Prediction of 2D Normal Strain Field in Concrete Structures
Concrete exhibits time-dependent long-term behavior driven by creep and shrinkage. These rheological effects are difficult to predict due to their stochastic nature and dependence on loading history. Existing empirical models used to predict rheological effects are fitted to databases composed largely of laboratory tests of limited time span and that do not capture differential rheological effects. A numerical model is typically required for application of empirical constitutive models to real structures. Notwithstanding this, the optimal parameters for the laboratory databases are not necessarily ideal for a specific structure. Data-driven approaches using structural health monitoring data have shown promise towards accurate prediction of long-term time-dependent behavior in concrete structures, but current approaches require different model parameters for each sensor and do not leverage geometry and loading. In this work, a physics-informed data-driven approach for long-term prediction of 2D normal strain field in prestressed concrete structures is introduced. The method employs a simplified analytical model of the structure, a data-driven model for prediction of the temperature field, and embedding of neural networks into rheological time-functions. In contrast to previous approaches, the model is trained on multiple sensors at once and enables the estimation of the strain evolution at any point of interest in the longitudinal section of the structure, capturing differential rheological effects.  more » « less
Award ID(s):
Author(s) / Creator(s):
Date Published:
Journal Name:
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Concrete exhibits long-term time-dependent behavior due to creep and shrinkage that impacts the safety and serviceability of high-rise buildings. These rheological effects are difficult to predict due to their random nature, dependence on environmental conditions, and loading history. Current methods include the use of creep and shrinkage tests of structure-specific concrete samples to update compliance and shrinkage, and sophisticated numerical models for prediction of the long-term structural behavior. However, creep and shrinkage tests are time-consuming, and simulation requires reliable numerical models and often proprietary solvers that are not available to the structural health monitoring (SHM) practitioner. Further, uncertainty propagation in complex numerical models is rarely seen in the relevant literature. In contrast, data-driven prediction methods using SHM data and simplified analytical models have shown to be successful for prestressed concrete bridges. In this work, we investigate calibration strategies of creep and shrinkage models using SHM data toward data-driven forecasting of long-term time-dependent behavior of high-rise buildings. A calibration strategy is identified that enables significant and consistent improvement of forecasting of long-term time-dependent behavior. It is also shown that continuous calibration can provide good predictions at least 30 days ahead. First-order analytical uncertainty propagation formulas are also provided. The calibration strategies are evaluated on data from two residential high-rise buildings in Singapore. Recommendations to the SHM practitioners are also given.

    more » « less

    The chemo-mechanical loading of rocks causes the dissolution and precipitation of multiple phases in the rock. This dissolution and precipitation of load-bearing mineral phases lead to the stress redistribution in neighboring phases, which in turn results in deformational changes of the sample composite. The aim of this study is to investigate the link between microstructural evolution and creep behavior of shale rocks subjected to chemo-mechanical loading through modeling time-dependent deformation induced by the dissolution-precipitation process. The model couples the microstructural evolution of the shale rocks with the stress/strain fields inside the material as a function of time. The modeling effort is supplemented with an experimental study where shale rocks were exposed to CO2-rich brine under high temperature and pressure conditions. 3D snapshots of the sample microstructure were generated using segmented micro-CT images of the shale sample. The time-evolving microstructures were then integrated with the Finite element-based mechanical model to simulate the creep induced by dissolution and precipitation processes independent of the intrinsic viscoelasticity/viscoplasticity of the mineral phases. After computation of the time-dependent viscoelastic properties of the shale composite, the combined microstructure model and finite element model were utilized to predict the time-dependent stress and strain fields in different zones of reacted shale.


    Determination of viscous behavior of shale rocks is key in wide range of applications such as stability of reservoirs, stability of geo-structures subjected to environmental forcing, underground storage of hazardous materials and hydraulic fracturing. Short-term creep strains in hydraulic fracturing can change stress fields and in turn can impact the hydraulic fracturing procedures(H. Sone & Zoback, 2010; Hiroki Sone & Zoback, 2013). While long-term creep strains can hamper the reservoir performance due to the reduction in permeability of the reservoir by closing of fractures and fissures(Du, Hu, Meegoda, & Zhang, 2018; Rybacki, Meier, & Dresen, 2016; Sharma, Prakash, & Abedi, 2019; Hiroki Sone & Zoback, 2014). Owing to these significance of creep strain, it is important to understand the viscoelastic/viscoplastic behavior of shales.

    more » « less
  3. Abstract

    In order to accurately predict the performance of materials under dynamic loading conditions, models have been developed that describe the rate-dependent material behavior and irrecoverable plastic deformation that occurs at elevated strains and applied loads. Most of these models have roots in empirical fits to data and, thus, require the addition of specific parameters that reflect the properties and response of specific materials. In this work, we present a systematic approach to the problem of calibrating a Johnson-Cook plasticity model for 304L stainless steel using experimental testing in which the parameters are treated as dependent on the state of the material and uncovered using experimental data. The results obtained indicate that the proposed approach can make the presence of a discrepancy term in calibration unnecessary and, at the same time, improve the prediction accuracy of the model into new input domains and provide improved understanding of model bias compared to calibration with stationary parameter values.

    more » « less
  4. Abstract

    Rate‐ and state‐friction (RSF) is an empirical framework that describes the complex velocity‐, time‐, and slip‐dependent phenomena observed during frictional sliding of rocks and gouge in the laboratory. Despite its widespread use in earthquake nucleation and recurrence models, our understanding of RSF, particularly its time‐ and/or slip‐dependence, is still largely empirical, limiting our confidence in extrapolating laboratory behavior to the seismogenic zone. While many microphysical models have been proposed over the past few decades, none have explicitly incorporated the effects of strain hardening, anelasticity, or transient elastoplastic rheology. Here we present a new model of rock friction that incorporates these phenomena directly from the microphysical behavior of lattice dislocations. This model of rock friction exhibits the same logarithmic dependence on sliding velocity (strain rate) as RSF and displays a dependence on the internal backstress caused by long‐range interactions among geometrically necessary dislocations (GNDs). Changes in the backstress (internal stress) evolve exponentially with plastic strain of asperities and are dependent on both the current backstress and previous deformation, which give rise to phenomena consistent with interpretations of the “critical slip distance,” “memory effect,” and “evolution effect” of RSF. The rate dependence of friction in this model is primarily controlled by the evolution of backstress and temperature. We provide several analytical predictions for RSF‐like behavior and the “brittle‐ductile” transition based on microphysical mechanisms and measurable parameters such as the GND density and strain‐dependent hardening modulus.

    more » « less
  5. Structures experience large vibrations and stress variations during their life cycles. This causes reduction in their load-carrying capacity which is the main design criteria for many structures. Therefore, it is important to accurately establish the performance of structures after construction that often needs full-field strain or stress measurements. Many traditional inspection methods collect strain measurements by using wired strain gauges. These strain gauges carry a high installation cost and have high power demand. In contrast, this paper introduces a new methodology to replace this high cost with utilizing inexpensive data coming from wireless sensor networks. The study proposes to collect acceleration responses coming from a structure and give them as an input to deep learning framework to estimate the stress or strain responses. The obtained stress or strain time series then can be used in many applications to better understand the conditions of the structures. In this paper, designed deep learning architecture consists of multi-layer neural networks and Long Short-Term Memory (LSTM). The network achieves to learn the relationship between input and output by exploiting the temporal dependencies of them. In the evaluation of the method, a three-story steel building is simulated by using various dynamic wind and earthquake loading scenarios. The acceleration time histories under these loading cases are utilized to predict the stress time series. The learned architecture is tested on acceleration time series that the structure has never experienced. 
    more » « less