scholarly journals THE INFLUENCE OF DEGRADATION SPEED ON ESTIMATION OF RESIDUAL RESOURCE OF REINFORCED CONCRETE HIGHWAY BRIDGES IN UKRAINE

2019 ◽  
pp. 10-18
Author(s):  
A. DAVYDENKO
Keyword(s):  
Reinforced Concrete ◽  
Prediction Model ◽  
Prediction Error ◽  
Degradation Rate ◽  
Control Parameter ◽  
Highway Bridges ◽  
Theoretical Research ◽  
Natural Rate ◽  
Practical Tool ◽  
First Time

Purpose. Analyze the effect of degradation rate on estimation of a remaining lifetime of Ukrainian highway bridges. Methods. Theoretical research. Results. The significant effect of degradation rate on the estimation of a remaining lifetime as the only control parameter of the Markov prediction model is proved. Originality. For the first time, the prediction error range of a remaining lifetime have been set at a constant natural rate of degradation. Practical value. The obtained results are a practical tool for managing the reliability and resource of reinforced concrete highway bridges.

Dimension and Frequency Profiles of Concrete Highway Bridges in New Madrid Region of Southeastern Missouri

1997 ◽  
Vol 1594 (1) ◽  
pp. 84-93 ◽  
Author(s):  
Ralph Alan Dusseau
Keyword(s):  
Reinforced Concrete ◽  
Concrete Slab ◽  
Highway Bridges ◽  
Ambient Vibration ◽  
Earthquake Hazards ◽  
Empirical Formulas ◽  
Reinforced Concrete Slab ◽  
Box Girder ◽  
Bridge Spans ◽  
New Madrid

The results of a study funded by the U.S. Geological Survey as part of the National Earthquake Hazards Reduction Program are presented. The first objective of this study was the development of a database for all 211 highway bridges along I-55 in the New Madrid region of southeastern Missouri. Profiles for five key dimension parameters (which are stored in the database) were developed, and the results for concrete highway bridges are presented. The second objective was to perform field ambient vibration analyses on 25 typical highway bridge spans along the I-55 corridor to determine the fundamental vertical and lateral frequencies of the bridge spans measured. These 25 spans included six reinforced concrete slab spans and two reinforced concrete box-girder spans. The third objective was to use these bridge frequency results in conjunction with the dimension parameters stored in the database to develop empirical formulas for estimating bridge fundamental natural frequencies. These formulas were applied to all 211 Interstate highway bridges in southeastern Missouri. Profiles for both fundamental vertical and lateral frequencies were then developed, and the results for concrete highway bridges are presented.

scholarly journals A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

scholarly journals Analyzing of Coatings on Steel - Reinforced Concrete Elements by Mobile NMR

2016 ◽  
Vol 62 (1) ◽  
pp. 65-82 ◽  
Author(s):  
J. Orlowsky
Keyword(s):  
Reinforced Concrete ◽  
Potential Effect ◽  
Building Site ◽  
Steel Reinforced Concrete ◽  
Mobile Nmr ◽  
The Individual ◽  
First Time ◽  
Concrete Elements ◽  
Non Destructive

Abstract A large number of infrastructural concrete buildings are protected against aggressive environments by coating systems. The functionality of these coating systems is mainly affected by the composition and thickness of the individual polymeric layers. For the first time ever, a mobile nuclear magnetic resonance (NMR) sensor allows a non-destructive determination of these important parameters on the building site. However, before this technique can be used on steel-reinforced concrete elements, the potential effect of the reinforcement on the measurement, i.e. the NMR signal, needs to be studied. The results show a shift of the NMR profile as well as an increase of the signals amplitude in the case of the reinforced samples, while calculating the thickness of concrete coating leading to identical results.

Seismic behavior of reinforced concrete sacrificial exterior shear keys of highway bridges

2017 ◽  
Vol 139 ◽  
pp. 59-70 ◽  
Author(s):  
Qiang Han ◽  
Yulong Zhou ◽  
Yuchen Ou ◽  
Xiuli Du
Keyword(s):  
Reinforced Concrete ◽  
Seismic Behavior ◽  
Highway Bridges ◽  
Shear Keys

In-Plane Force Effect of Reinforced Concrete Beam with Elastic Supports

2011 ◽  
Vol 287-290 ◽  
pp. 1896-1901
Author(s):  
Zhi Kun Guo ◽  
Wan Xiang Chen ◽  
Qi Fan Wang ◽  
Yu Huang ◽  
Chao Pu Li ◽  
...  
Keyword(s):  
Reinforced Concrete ◽  
Concrete Beam ◽  
Reinforced Concrete Beam ◽  
Reinforced Concrete Beams ◽  
Nonlinear Characteristics ◽  
Elastic Supports ◽  
Calculation Results ◽  
Basic Equations ◽  
First Time ◽  
Good Agreement

The bearing capacities of one-way reinforced concrete beams with elastic supports are investigated in this paper. According to the nonlinear characteristics of the beams, the basic equations based on plastic theory of concrete are derived by considering the in-plane force effects that aroused by the constraints of supports when the beams deforming. It is indicated that the calculation results are in good agreement with experimental datum, and the influences of different supports on the bearing capacities of the beams are quantitatively given for the first time.

scholarly journals Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chaohui Wang ◽  
Songyuan Tan ◽  
Qian Chen ◽  
Jiguo Han ◽  
Liang Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Multiple Regression ◽  
Dynamic Modulus ◽  
Prediction Error ◽  
Prediction Models ◽  
Asphalt Mixture ◽  
High Modulus ◽  
Output Stability ◽  
Svm Model

Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high-modulus asphalt mixture. Five high-temperature performance indexes of high-modulus asphalt and its mixture were selected. The correlation between the above five indexes and the dynamic modulus of the high-modulus asphalt mixture was analyzed. On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. According to parameter adjustment and cross-validation, the output stability and accuracy of different prediction models were compared and evaluated. The most effective prediction model was recommended. The results show that the SVM model has more significant prediction accuracy and output stability than the multiple regression model and the GRNN model. Its prediction error was 0.98–9.71%. Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. The SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture.

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