A Bayesian inference framework for predicting the long-term deflection of concrete structures caused by creep and shrinkage

Han, Bing; Xiang, Tianyu; Xie, Huibing

doi:10.1016/j.engstruct.2017.03.055

Cited by 36 publications

(13 citation statements)

References 21 publications

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“…In this section, we propose a general scheme of DSS for the Colle Isarco viaduct, in which information coming from the 1D FE model are also involved. As depicted in Figure , the proposed DSS consists of two main parts: (i) Bayesian logic (Sivia and Skilling, ; Bolstad, ; Han et al., ) to compute probabilities of structural states that may occur; and (ii) axiomatic Expected Utility Theory (EUT) (Neumann and Morgenstern, ; Raiffa and Schlaifer, ) to identify economically optimal choices. To the best of the authors’ knowledge, no DSS based on EUT has yet been proposed for everyday use in the field of civil engineering.…”

Section: Development Of a Decision Support Systemmentioning

confidence: 99%

“…The SHM system installed on the Colle Isarco viaduct includes: (i) fiber‐optic sensors based on fiber Bragg gratings (Glisic and Inaudi ; Balageas et al., ) to measure strains of top and bottom slabs; (ii) PT100 resistance thermometers to acquire temperature variations along the whole structure; and (iii) a topographic network with prisms to measure displacements. This fusion of data coming from different sensors certainly reduces uncertainties regarding structural behavior (Han et al., ), helps the bridge manager to identify causes of possible anomalies, and improves his or her capability to take optimal decisions (Cappello et al., ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Viscoelastic Model for the Long‐Term Deflection of Segmental Prestressed Box Girders

Beltempo

Bursi

Cappello

et al. 2017

Computer aided Civil Eng

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Section: Development Of a Decision Support Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Viscoelastic Model for the Long‐Term Deflection of Segmental Prestressed Box Girders

Beltempo

Bursi

Cappello

et al. 2017

Computer aided Civil Eng

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“…Međutim, različite teorije puzanja često daju različite rezultate, pa zbog toga Han sa suradnicima [35] predlaže stohastičko/ probabilistički model određivanja dugotrajnih progiba koji se bazira na kratkotrajnim progibima. Danas se za proračun vremenski ovisnih deformacija betona najčešće koriste empirijski modeli, koji su uglavnom vezani za nacionalne propise.…”

Section: Zaključakunclassified

An overview of the models for time dependent behavior of concrete

Džolan¹

2017

Zajednički Temelji 2017 - Peti Skup Mladih Istraživača Iz Područja Građevinarstva I Srodnih Tehničkih Znanosti - Zbornik Radova

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SažetakBeton kao građevni materijal, u današnjem obliku, počeo se koristiti krajem 19. stoljeća. Međutim, tlačna čvrstoća betona znatno je veća od vlačne, te se beton armira kako bi armatura preuzela naprezanja u vlačnoj zoni betona. U vlačnoj zoni armiranog betona dolazi do otvaranja pukotina u betonu kada su vlačna naprezanja dostigla vrijednost vlačne čvrstoće betona. Kako bi se izbjegla pojava pukotina ili ograničila njihova širina, osmišljeno je prednapinanje betona. Prednapinjanjem se u beton unose tlačna naprezanja prije njegova opterećivanja, te se time omogućuje tlačna raspodjela naprezanja po visini opterećenog betonskog presjeka, čime se izbjegava pojava pukotina. Prvi pokušaji prednapinjanja su propadali zbog nepoznavanja fenomena puzanja i skupljanja betona. Naime, u betonu se uslijed puzanja i skupljanja pojavljuju naprezanja koja mogu dovesti do otvaranja pukotine prije opterećivanja betona. Puzanje i skupljanje predstavljaju vremenski ovisne deformacije, gdje puzanje, za razliku od skupljanja, ovisi i o opterećenju. Deformacije izazvane puzanjem i skupljanjem mogu biti i nekoliko puta veće od deformacija izazvanih djelovanjem kratkotrajnih statičkih opterećenja, tako da je besmisleno proučavati ponašanje konstrukcija zanemarujući efekte puzanja i skupljanja betona. Ključne riječi: puzanje, skupljanje, prednapinjanje, betonAn overview of the models for time dependent behavior of concrete Abstract Using of the concrete as building material in its present form began at the end of the 19th century. However, the compressive strength of the concrete is considerably higher than the tensile strength and the concrete is reinforced that the reinforcement takes the stresses in the tensile zone of concrete. In the tensile zone of reinforced concrete, concrete cracking occurs when tensile stresses have reached the value of tensile strength of concrete. In order to avoid cracking or limiting their width, the idea of pre-stressing concrete appears. By prestressing, compresive pressures are applied to the concrete before loading the concrete, thereby it is provided compresive pressure distribution at the height of the loaded concrete section, avoiding the occurrence of cracks. The first attempts prestressing haven't been succeed because it was unknown phenomenon of creep and shrinkage concrete. Namely, there are stresses in concrete that are caused with creep and shrinkage and that can cause cracking of concrete before loading of concrete. Creep and shrinkage represent time-dependent deformations, where creep, unlike shrinkage, also depends of the load. Deformations caused by creep and shrinkage may also be several times greater than deformations caused by the action of short-term static loads, so it is meaningless to study the behavior of the structures by neglecting the effects of creep and shrinkage of concrete.

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“…Sajedi et al [ 4 ] proposed an analytical procedure that can predict the flexural behavior of intact and corroded reinforced concrete beams, with or without lap splices considering the bond stress-slip behavior at the steel–concrete interface. Han et al [ 5 ] built a new computational framework for Bayesian inference regarding the long-term deflection of concrete structures, importance sampling technology, and response surface approach, and a stochastic process was introduced to improve computational efficiency and describe the random properties of creep. As the solution of nonlinear differential equations of complex structures is arduous to realize, the above-mentioned prediction methods often rely on numerical simulation models.…”

Section: Introductionmentioning

confidence: 99%

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

Xin

Zhou

Yang

et al. 2018

Sensors

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Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

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A Bayesian inference framework for predicting the long-term deflection of concrete structures caused by creep and shrinkage

Cited by 36 publications

References 21 publications

A Viscoelastic Model for the Long‐Term Deflection of Segmental Prestressed Box Girders

A Viscoelastic Model for the Long‐Term Deflection of Segmental Prestressed Box Girders

An overview of the models for time dependent behavior of concrete

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

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