Mechanical Behavior of Fiber-Reinforced Self-Compacting Rubberized Concrete Exposed to Elevated Temperatures

Aslani, Farhad; Sun, Junbo; Huang, Guanqi

doi:10.1061/(asce)mt.1943-5533.0002942

Cited by 41 publications

(12 citation statements)

References 48 publications

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“…Sensing technologies are also employed in the experimental investigation on rehabilitation of corroded reinforced concrete columns [ 8 , 9 , 10 ]. For structure built with new cement materials such as solid wastes incorporated concrete, the structural monitoring is rather important [ 11 , 12 , 13 , 14 , 15 ]. By combining with artificial intelligence, the data derived from sensors can be efficiently and effectively analysed to guide the design the construction [ 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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Sensing technologies present great improvements in construction performance including the safety, productivity, and quality. However, the corresponding applications in real projects are far behind compared with the academically research. This research aims to discover dominate influence factors in the sensing technologies adoption and ultimately develop a governance framework facilitating adoption processes. The framework is dedicated on general sensing technologies rather than single sensor in previous framework studies. To begin with, the influence factors of sensing technologies and other similar emerging technologies are summarised through a review. Then, a mixed methods design was employed to collect quantitative data through an online survey, and qualitative data through semi-structured interviews. Findings of the quantitative method reveal that the most widely implemented sensing technologies are GPS and visual sensing technology, but they’re still not adopted by all construction companies. Partial Least Squares Structural Equation Modelling reveals that supplier characteristics have the highest effect in all influence factors. Qualitative method was adopted to investigate perceptions of construction stakeholders on the major decision-making considerations in the adoption process. Ultimately, a triangulation analysis of findings from the literature review, online survey and interviews resulted in the governance framework development. The overarching contribution of this research focus on the general adoption of sensing technologies rather than the adoption of a specific sensor. Therefore, the governance framework can assist with the decision-making process of any sensing technology adoption in construction.

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Section: Introductionmentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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“…[16][17][18][19][20][21][22][23]. Besides, solid waste materials, such as waste glass and recycled aggregate, have the potential to be applied in HSC to overcome the strength shortcomings of the waste itself [24][25][26][27][28][29][30][31]. Therefore, HSC incorporated with solid wastes has the benefits of both strength enhancement and sustainable prospect [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

et al. 2022

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High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

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“…First, the distributed fiber generates random intersection in the specimen, which represents characteristics of preventing crack generation and suppressing brittle destruction [12][13][14]. Mechanical strengths, therefore, enhance significantly as evidenced by the 115% increment of UCS [15,16]. Previous studies have proven likewise promotion with various fibrous materials [17,18].…”

Section: Introductionmentioning

confidence: 99%

Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber

Ding

Wang

et al. 2022

Materials

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Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses the feasible combined enhancement of unconfined compressive strength (UCS) and flexural strength (FS) of construction and demolition (C&D) waste, polypropylene fiber, and sodium sulfate. Moreover, machine learning (ML) techniques including Back Propagation Neural Network (BPNN) and Random Forest (FR) were applied to estimate UCS and FS based on the comprehensive dataset. The laboratory tests were conducted at 7-, 14-, and 28-day curing age, indicating the positive effect of cement, C&D waste, and sodium sulfate. The improvement caused by polypropylene fiber on FS was also evaluated from the 81 experimental results. In addition, the beetle antennae search (BAS) approach and 10-fold cross-validation were employed to automatically tune the hyperparameters, avoiding tedious effort. The consequent correlation coefficients (R) ranged from 0.9295 to 0.9717 for BPNN, and 0.9262 to 0.9877 for RF, respectively, indicating the accuracy and reliability of the prediction. K-Nearest Neighbor (KNN), logistic regression (LR), and multiple linear regression (MLR) were conducted to validate the BPNN and RF algorithms. Furthermore, box and Taylor diagrams proved the BAS-BPNN and BAS-RF as the best-performed model for UCS and FS prediction, respectively. The optimal mixture design was proposed as 30% cement, 20% C&D waste, 4% fiber, and 0.8% sodium sulfate based on the importance score for each variable.

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Mechanical Behavior of Fiber-Reinforced Self-Compacting Rubberized Concrete Exposed to Elevated Temperatures

Cited by 41 publications

References 48 publications

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber

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