scholarly journals Statistical Inversion Approach for Stress Estimation Based on Strain Monitoring in Continuously Pre-Stressed Concrete Beams

2021 ◽  
Vol 11 (21) ◽  
pp. 10161
Author(s):  
Huibing Xie ◽  
Bing Han ◽  
Wutong Yan ◽  
Peng Jiang

Stress is one of the most important physical indexes reflecting the mechanical behavior of concrete structures. In general, stress in structures cannot be directly monitored and can only be estimated through an established model of stress and strain. The accuracy of the estimated stress depends on the rationality of the established model for stress and strain. As the strain measured by sensors contains creep, shrinkage, and elastic strain, it is difficult to establish an analytical model for strain and stress. In this paper, a statistical inverse method was utilized to estimate the stress in continuously pre-stressed concrete beams based on the monitored strain. Stress in the beams and the model uncertainty factors were treated as model parameters. A linear-simplified method was adopted to determine the prior distribution of the stresses. The posterior distribution of the stresses at different locations during bridge construction can be obtained by the proposed method. A continuously pre-stressed concrete beam bridge was taken as the case study to verify the effectiveness of the proposed method. Additionally, the constitution of the total strain in the different construction stages was calculated. It was concluded that the creep strain is the dominant part of the total strain.

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.


1993 ◽  
Vol 317 ◽  
Author(s):  
R.M. Osgood ◽  
B.M. Clemens ◽  
R.L. White ◽  
S. Brennan

ABSTRACTGrazing incidence and asymmetric X-ray diffraction were used to measure the stress and strain state of Fe(110)/Mo(110) Multilayers. The highest stress in the Fe constituent of the multilayer was along the [110] in-plane direction and was due to interaction with the substrate. The Magnetic anisotropy of the Fe Multilayer constituent was measured and the magnetic surface anisotropy, which favored in-plane [001] magnetization, was deduced. In contrast, the magnetic surface anisotropy of a single layer of Fe on W preferred in-plane [110] magnetization, in agreement with the Néel Model.


2021 ◽  
Author(s):  
◽  
Ivan Banović

The problem under consideration is the earthquake impact on structures. The subject of the performed research is the efficiency of seismic base isolation using layers of predominantly natural materials below the foundation, as well as the development of a numerical model for seismic analysis of structures with such isolation. The aseismic layers below foundation are made of limestone sand - ASL-1, stone pebbles - ASL-2, and stone pebbles combined with layers of geogrid and geomembrane - ASL-3. The experimental research methodology is based on the use of shake-table and other modern equipment for dynamic and static testing of structures. Experiments were conducted on the basis of detailed research plan and program. Efficiency of the limestone sand layer - ASL-1 was tested on cantilever concrete columns, under seismic excitations up to failure, varying the sand thickness and intensity of seismic excitation. Influence of several layer parameters on the efficiency of stone pebble layer - ASL-2 was investigated. For each considered layer parameter, a rigid model M0 was exposed to four different accelerograms, with three levels of peak ground acceleration (0.2 g, 0.4 g and 0.6 g), while all other layer parameters were kept constant. On the basis of test results, the optimal pebble layer was adopted. Afterwards, the optimal ASL-2 efficiency was tested on various model parameters: stiffness (deformable models M1-M4), foundation size (small and large), excitation type (four earthquake accelerograms), and stress level in the model (elastic and up to failure). In the ASL-3 composite aseismic layer, the optimal ASL-2 is combined with a thin additional layer of sliding material (geogrid, geomembrane above limestone sand layer), in order to achieve greater efficiency of this layer than that of the ASL-2. A total of eleven different aseismic layers were considered. To determine the optimal ASL-3, the M0 model was used, like for the ASL-2. On the basis of test results, the optimal ASL-3 layer was adopted (one higher strength geogrid at the pebble layer top). The optimal ASL-3 is tested on various model parameters, analogous to the optimal ASL-2. A numerical model for reliable seismic analysis of concrete, steel, and masonry structures with seismic base isolation using ASL-2 was developed, with innovative constitutive model for seismic isolation. The model can simulate the main nonlinear effects of mentioned materials, and was verified on performed experimental tests. In relation to the rigid base - RB without seismic isolation, model based on the ASL-1 had an average reduction in seismic force and strain/stress by approximately 10% at lower PGA levels and approximately 14% at model failure. Due to the effect of sand calcification over time, the long-term seismic efficiency of such a layer is questionable. It was concluded that the aseismic layers ASL-2 and ASL-3 are not suitable for models of medium-stiff structure M3 and soft structure M4. In relation to the RB without seismic isolation, the M1 (very stiff structure) and M2 (stiff structure) based on the ASL-2 had an average reduction in seismic force and strain/stress by approximately 13% at lower PGA levels and approximately 25% at model failure. In relation to the RB without seismic isolation, the M1 and M2 based on the ASL-3 had an average reduction in seismic force and strain/stress by approximately 25% at lower PGA levels and approximately 34% at model failure. In relation to the RB without seismic isolation, the ASL-2 and ASL-3 did not result in major M1 and M2 model displacements, which was also favourable. It is concluded that the ASL-2 and especially ASL-3 have great potential for seismic base isolation of very stiff and stiff structures, as well as small bridges based on solid ground, but further research is needed. In addition, it was concluded that the developed numerical model has great potential for practical application. Finally, further verification of the created numerical model on the results of other experimental tests is needed, but also improvement of the developed constitutive models.


Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.


Author(s):  
Feng Zhou ◽  
Jianxin (Roger) Jiao

Traditional user experience (UX) models are mostly qualitative in terms of its measurement and structure. This paper proposes a quantitative UX model based on cumulative prospect theory. It takes a decision making perspective between two alternative design profiles. However, affective elements are well-known to have influence on human decision making, the prevailing computational models for analyzing and simulating human perception on UX are mainly cognition-based models. In order to incorporate both affective and cognitive factors in the decision making process, we manipulate the parameters involved in the cumulative prospect model to show the affective influence. Specifically, three different affective states are induced to shape the model parameters. A hierarchical Bayesian model with a technique called Markov chain Monte Carlo is used to estimate the parameters. A case study of aircraft cabin interior design is illustrated to show the proposed methodology.


2015 ◽  
Vol 8 (10) ◽  
pp. 3441-3470 ◽  
Author(s):  
J. A. Bradley ◽  
A. M. Anesio ◽  
J. S. Singarayer ◽  
M. R. Heath ◽  
S. Arndt

Abstract. SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.


2015 ◽  
Vol 12 (12) ◽  
pp. 13217-13256 ◽  
Author(s):  
G. Formetta ◽  
G. Capparelli ◽  
P. Versace

Abstract. Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.


2021 ◽  
Vol 2 (3) ◽  
pp. 74-98
Author(s):  
Peter Hugo Nelson

ABSTRACT Students develop and test simple kinetic models of the spread of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Microsoft Excel is used as the modeling platform because it is nonthreatening to students and it is widely available. Students develop finite difference models and implement them in the cells of preformatted spreadsheets following a guided inquiry pedagogy that introduces new model parameters in a scaffolded step-by-step manner. That approach allows students to investigate the implications of new model parameters in a systematic way. Students fit the resulting models to reported cases per day data for the United States using least squares techniques with Excel's Solver. Using their own spreadsheets, students discover for themselves that the initial exponential growth of COVID-19 can be explained by a simplified unlimited growth model and by the susceptible-infected-recovered (SIR) model. They also discover that the effects of social distancing can be modeled using a Gaussian transition function for the infection rate coefficient and that the summer surge was caused by prematurely relaxing social distancing and then reimposing stricter social distancing. Students then model the effect of vaccinations and validate the resulting susceptible-infected-recovered-vaccinated (SIRV) model by showing that it successfully predicts the reported cases per day data from Thanksgiving through the holiday period up to 14 February 2021. The same SIRV model is then extended and successfully fits the fourth peak up to 1 June 2021, caused by further relaxation of social distancing measures. Finally, students extend the model up to the present day (27 August 2021) and successfully account for the appearance of the delta variant of the SARS-CoV-2 virus. The fitted model also predicts that the delta variant peak will be comparatively short, and the cases per day data should begin to fall off in early September 2021, counter to current expectations. This case study makes an excellent capstone experience for students interested in scientific modeling.


2011 ◽  
Vol 403-408 ◽  
pp. 3758-3762
Author(s):  
Subhajit Patra ◽  
Prabirkumar Saha

In this paper, two efficient control algorithms are discussed viz., Linear Quadratic Regulator (LQR) and Dynamic Matrix Controller (DMC) and their applicability has been demonstrated through case study with a complex interacting process viz., a laboratory based four tank liquid storage system. The process has Two Input Two Output (TITO) structure and is available for experimental study. A mathematical model of the process has been developed using first principles. Model parameters have been estimated through the experimentation results. The performance of the controllers (LQR and DMC) has been compared to that of industrially more accepted PID controller.


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