scholarly journals Influence Factors and Prediction Model of Bond Strength of Tunnel Fireproof Coating under Freeze–Thaw Cycles

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1544
Author(s):  
Guanji Lu ◽  
Tao Ji ◽  
Binbin Zhang ◽  
Yu Ma ◽  
Cong Liao
Keyword(s):  
Bond Strength ◽  
Beneficial Effect ◽  
Prediction Model ◽  
Polypropylene Fiber ◽  
Influence Factors ◽  
Freeze Thaw ◽  
Damage Degree ◽  
Ultrasonic Sound ◽  
Bonding Property ◽  
Fireproof Coating

The freeze–thaw resistant performance of a tunnel fireproof coating (TFC) has an important impact on bonding property and durability. The influence of redispersible emulsion powder, polypropylene fiber and air-entraining agent on TFCs was studied. Transverse fundamental frequency and ultrasonic sound velocity were used to evaluate the damage degree of TFC, and the mechanism was revealed by SEM and pore structure. The results show that the most beneficial effect on bond strength of TFC is redispersible emulsion powder, followed by air-entraining agent, and then polypropylene fiber. After freeze–thaw cycles, the cumulative pore volume of micropores in the TFC increases obviously, while the porosity of macropores does not change significantly. A prediction model was proposed, which can calculate the bond strength from the damage degree of TFC under freeze–thaw cycles. The achievement can promote the application of TFC in cold regions.

Optimization Research of Mix Proportion of Frost Resistance Construction of Concrete Based on the MATLAB Language

2014 ◽  
Vol 584-586 ◽  
pp. 1917-1921
Author(s):  
Jun Jie Zhang ◽  
Rui Hong Shao ◽  
Xiang Yi Meng
Keyword(s):  
Compressive Strength ◽  
Objective Function ◽  
Frost Resistance ◽  
Unit Volume ◽  
Influence Factors ◽  
Multi Objective Optimization ◽  
Cold Region ◽  
Freeze Thaw ◽  
Performance Indexes ◽  
Mix Proportion

Analyze the influence factors of mix proportion affecting concrete freeze-thaw damage. Use the five main performance indexes of the concrete, which are compressive strength, strength of extension, impermeability grade, and frost resistance grade and per unit volume cost concrete, as the objective function of multi-objective optimization equation. Invoke the fgoalattain function in the MATLAB Optimization Toolbox to solve. The optimized parameters of mix proportion of frost resistance construction of unit concrete in cold region are: concrete 1532.6kg, water 910kg, sand 5510.6kg, 5-20mm cobblestone 3747.2kg、20-40mm cobblestone 3658.6kg、40-80mm cobblestone 4733.5kg、80-150mm cobblestone 4738.1kg, and the dosage of water reducing agent is 7.3kg.

Study of a strength prediction model of unsaturated compacted expansive soil under freeze–thaw cycles

2022 ◽  
Vol 15 (2) ◽  
Author(s):  
Zhongnian Yang ◽  
Qi Zhang ◽  
Wei Shi ◽  
Zhaochi Lu ◽  
Zhibin Tu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Expansive Soil ◽  
Strength Prediction ◽  
Freeze Thaw

scholarly journals Influence of the Fiber Volume Content on the Durability-Related Properties of Polypropylene-Fiber-Reinforced Concrete

2020 ◽  
Vol 12 (2) ◽  
pp. 549
Author(s):  
Chenfei Wang ◽  
Zixiong Guo ◽  
Ditao Niu
Keyword(s):  
Reinforced Concrete ◽  
Flexural Strength ◽  
Volume Content ◽  
Polypropylene Fiber ◽  
Fiber Reinforced Concrete ◽  
Fiber Reinforced ◽  
Fiber Volume ◽  
Freeze Thaw ◽  
Chloride Environment ◽  
Polypropylene Fiber Reinforced Concrete

Polypropylene-fiber-reinforced concrete impacts the early shrinkage during the plastic stage of concrete, and the fiber volume content influences the durability-related properties of concrete. The purpose of this paper was to investigate the influence of fiber volume content on the mechanical properties, durability, and chloride ion penetration of polypropylene-fiber-reinforced concrete in a chloride environment. Tests were carried out on cubes and cylinders of polypropylene-fiber-reinforced concrete with polypropylene fiber contents ranging from 0% to 0.5%. Extensive data from flexural strength testing, dry–wet testing, deicer frost testing, and chloride penetration testing were recorded and analyzed. The test results show that the addition of the fiber improves the failure form of the concrete specimens, and 0.1% fiber content maximizes the compactness of the concrete. The flexural strength of specimen C2 with 0.1% fiber shows the highest strength obtained herein after freeze–thaw cycling, and the water absorption of specimen C2 is also the lowest after dry–wet cycling. The results also indicate that increasing the fiber volume content improves the freeze–thaw resistance of the concrete in a chloride environment. Chlorine ions migrate with the moisture during dry–wet and freeze–thaw cycling. The chlorine ion diffusion coefficient (Dcl) increases with increasing fiber content, except for that of specimen C2 in a chloride environment. The Dcl during freeze–thaw cycling is much higher than that during dry–wet cycling.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

scholarly journals Mechanical Properties of Sandstone Cement-Stabilized Macadam

2019 ◽  
Vol 9 (17) ◽  
pp. 3460 ◽  
Author(s):  
Qiang Du ◽  
Ting Pan ◽  
Jing Lv ◽  
Jie Zhou ◽  
Qingwei Ma ◽  
...  
Keyword(s):  
Cement Content ◽  
Influence Factors ◽  
Road Construction ◽  
Experimental Investigations ◽  
Freezing And Thawing ◽  
Freeze Thaw ◽  
Test Pieces ◽  
Thaw Cycle ◽  
Freeze Thaw Cycle ◽  
Brazilian Splitting

Application of sandstone in cement-stabilized macadam (CSM) is an effective way to utilize sandstone. To determine the feasibility of using sandstone as a CSM aggregate, a series of experimental investigations, such as unconfined compressive strength (UCS) tests, Brazilian splitting tests and freeze-thaw cycle tests, were conducted on sandstone cement-stabilized macadam (SCSM). Three mixed variables, covering the cement content, aggregate type and curing period, were set as influence factors. The testing results indicated that the UCS, indirect tensile strength (ITS) and frost resistance property of the test-pieces increased with cement content and curing age. Considering the asphalt pavement design specifications for China, the UCS and ITS values of the SCSM complied with the requirements of light traffic road construction before freeze-thaw cycles. However, the SCSM subjected to freezing and thawing meets the requirements only when the cement content is 4.5%. Therefore, it is noteworthy that CSM containing sandstone aggregates should be applied with caution in cold region because of insufficient freeze resistance.

Damage and Springback Analysis of Two Typical Dented Pipelines With Different Parameters

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Ying Wu ◽  
Jiacheng Li ◽  
Linya Li
Keyword(s):  
Internal Pressure ◽  
Influence Factors ◽  
Influential Factors ◽  
Orthogonal Experimental Design ◽  
Damage Degree ◽  
Gray Correlation Degree ◽  
Correlation Degree ◽  
Gray Correlation ◽  
Nonlinear Regression Method ◽  
Dent Depth

During pipeline construction, the pipeline may be impacted by sharp rocks or excavators. To study the failure mechanism of the pipeline, the damage degree and springback rate of the pipelines with two typical dents (transverse and longitudinal) were analyzed in terms of various factors (indenter size, pipeline size and internal pressure, and dent depth). The results reveal the following: (1) when pipeline size and internal pressure are unchanged and indenter size is changed, the integral value I used to measure the damage degree of the dented pipeline increases with increasing dent depth. When the dent depth reaches a certain value, at the same dent depth, the smaller the indenter size, the larger the damage integral value; (2) when other parameters remain unchanged, the larger the pipeline size is, the larger is the damage integral value, and the larger the internal pressure is, the smaller is the damage integral value. (3) The curves for damage and springback for the two kinds of dents are basically similar. Generally, the maximum damage of the longitudinal dent is larger than that of the transverse dent. (4) By a combination of an orthogonal experimental design and a gray correlation degree calculation, for the damage integral value of the two typical dented pipelines, the order of importance of the influential factors was obtained. (5) Formulas for the damage integral value and influence factors were fit using a nonlinear regression method, which provides a reference for calculation of pipeline damage.

scholarly journals Bond Characteristics of Macro Polypropylene Fiber in Cementitious Composites Containing Nanosilica and Styrene Butadiene Latex Polymer

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jae-Woong Han ◽  
Ji-Hong Jeon ◽  
Chan-Gi Park
Keyword(s):  
Bond Strength ◽  
Microstructural Analysis ◽  
Polypropylene Fiber ◽  
Fiber Surface ◽  
Cementitious Composites ◽  
Bond Properties ◽  
Interface Toughness ◽  
Latex Polymer ◽  
Styrene Butadiene ◽  
Bond Tests

This study evaluated the bond properties of polypropylene (PP) fiber in plain cementitious composites (PCCs) and styrene butadiene latex polymer cementitious composites (LCCs) at different nanosilica contents. The bond tests were evaluated according to JCI SF-8, in which the contents of nanosilica in the cement were 0, 2, 4, 6, 8, and 10 wt%, based on cement weight. The addition of nanosilica significantly affected the bond properties between macro PP fiber and cementitious composites. For PCCs, the addition of 0–2 wt% nanosilica enhanced bond strength and interface toughness, whereas the addition of 4 wt% or more reduced bond strength and interface toughness. The bond strength and interfacial toughness of LCCs also increased with the addition of up to 6% nanosilica. The analysis of the relative bond strength showed that the addition of nanosilica affects the bond properties of both PCC and LCC. This result was confirmed via microstructural analysis of the macro PP fiber surface after the bond tests, which revealed an increase in scratches due to frictional forces and fiber tearing.

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