Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Tanyıldızı, Harun

doi:10.1155/2018/5140610

Cited by 39 publications

(19 citation statements)

References 56 publications

(63 reference statements)

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“…With the rapid development of construction technology, concrete is frequently subjected to the harsh environments, and the concrete structure possibly suffers a fire-damage when the fire attack happened or using the concrete as the protection materials in the furnace [1][2][3][4]. Previous studies found that the concrete's mechanical properties decreased with an increase in the elevated temperatures, and the structural safety and service life reduced correspondingly [5,6].…”

Section: Introductionmentioning

confidence: 99%

Effects of High Temperature and Cooling Pattern on the Chloride Permeability of Concrete

Duan

2019

Advances in Civil Engineering

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Concrete structure is frequently subjected to the fire attack, whereas the permeability of concrete with fire-damage has received little consideration. is paper aims to investigate the chloride permeability of plain concrete and recycled aggregate concrete (RAC) with fire-damage, and the effects of various cooling patterns and recuring treatment on the chloride permeability are also studied. e results manifest that the elevated temperatures result in an increase in the fire-damage and chloride permeability of concrete, and that the increase becomes more obvious with the temperature above 400°C. Attributing to the water-cooling which provides a recuring environment, the chloride permeability after water-cooling is lower than that after air-cooling when the temperature is 200°C. Whereas when the temperature is above 400°C, the chloride permeability after water-cooling becomes higher than that after air-cooling, due to an extra damage that the water-cooling produces. e recuring treatment can reduce the chloride permeability of concrete with fire-damage, and the reduction becomes more significant when the concrete suffers a serious fire-damage. Exposing to the same condition of high temperature, the addition of recycled aggregate (RA) further boosts the fire-damage and chloride permeability of concrete. Particularly, the chloride permeability increases with the increasing of RA replacement ratios, linearly, and the increased temperatures further lead to an increase in the slope of the fitting straight line.

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Section: Introductionmentioning

confidence: 99%

Effects of High Temperature and Cooling Pattern on the Chloride Permeability of Concrete

Duan

2019

Advances in Civil Engineering

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“…Introduced by Vapnik [40], support vector machines (SVM) have gained attentions of the academic community and have become a preeminent pattern recognition approach [55,[80][81][82][83][84][85][86][87][88][89][90]. Given a data sample set S drawn from a data universe X U , a hidden target function f: X ⟶ 0, 1 { }, we first create a labeled training dataset D, where D � (x, y)|x ∈ S and y � f(x) .…”

Section: Support Vector Machinementioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

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“…This algorithm simulates the knowledge acquisition and inference processes of the human brain [37]. Based on previous studies [38][39][40][41], BPANN is proved to be highly effective in dealing with complex nonlinear data modeling problems. A BPANN consists of the input, hidden, and output layers.…”

Section: Backpropagation Artificial Neural Network (Bpann)mentioning

confidence: 99%

Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

Hoang

Nguyen

2018

Mathematical Problems in Engineering

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Periodic surveys of asphalt pavement condition are very crucial in road maintenance. This work carries out a comparative study on the performance of machine learning approaches used for automatic pavement crack recognition. Six machine learning approaches, Naïve Bayesian Classifier (NBC), Classification Tree (CT), Backpropagation Artificial Neural Network (BPANN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), and Least Squares Support Vector Machine (LSSVM), have been employed. Additionally, Median Filter (MF), Steerable Filter (SF), and Projective Integral (PI) have been used to extract useful features from pavement images. In the feature extraction phase, performance comparison shows that the input pattern including the diagonal PIs enhances the classification performance significantly by creating more informative features. A simple moving average method is also employed to reduce the size of the feature set with positive effects on the model classification performance. Experimental results point out that LSSVM has achieved the highest classification accuracy rate. Therefore, this machine learning algorithm used with the feature extraction process proposed in this study can be a very promising tool to assist transportation agencies in the task of pavement condition survey.

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Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Cited by 39 publications

References 56 publications

Effects of High Temperature and Cooling Pattern on the Chloride Permeability of Concrete

Effects of High Temperature and Cooling Pattern on the Chloride Permeability of Concrete

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

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