Probabilistic assessment of axial load-carrying capacity of FRCM-strengthened concrete columns using artificial neural network and Monte Carlo simulation

Irandegani, Mohammad Ali; Zhang, Daxu; Shadabfar, Mahdi

doi:10.1016/j.cscm.2022.e01248

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…It has been demonstrated that ANNs can predict the compressive and tensile strength of concretes containing construction and agricultural wastes [ 32 , 48 ], blast furnace slag [ 35 ], and alkali-activated mortars [ 34 , 37 ]. Some authors combined an ANN with other techniques, such as a genetic algorithm (GA) [ 35 ], statistics and holistic models [ 44 ], the cuckoo search method [ 49 , 50 ], ANFIS models [ 24 , 51 ], fuzzy logic models [ 52 , 53 ], and the Monte Carlo approach [ 54 ], to optimize the prediction results. Jiang et al [ 53 ] and Farooq et al [ 55 ] studied the prediction of mechanical properties of self-compacting concretes and high-performance concretes using an ANN on over 1030 datasets.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

et al. 2022

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In the 21st century, numerous numerical calculation techniques have been discovered and used in several fields of science and technology. The purpose of this study was to use an artificial neural network (ANN) to forecast the compressive strength of waste-based concretes. The specimens studied include different kinds of mineral additions: metakaolin, silica fume, fly ash, limestone filler, marble waste, recycled aggregates, and ground granulated blast furnace slag. This method is based on the experimental results available for 1303 different mixtures gathered from 22 bibliographic sources for the ANN learning process. Based on a multilayer feedforward neural network model, the data were arranged and prepared to train and test the model. The model consists of 18 inputs following the type of cement, water content, water to binder ratio, replacement ratio, the quantity of superplasticizer, etc. The ANN model was built and applied with MATLAB software using the neural network module. According to the results forecast by the proposed neural network model, the ANN shows a strong capacity for predicting the compressive strength of concrete and is particularly precise with satisfactory accuracy (R² = 0.9888, MAPE = 2.87%).

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Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

et al. 2022

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“…The high applicability of ANN for simulating multi-parameter systems has received significant attention in many different fields (e.g., color coordinates in dyeing [7], energy storage tanks [8], and concrete [9,10]). Details of ANN and other soft computing technologies applications on mining engineering can be found in Jang and Topal (2014) [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

Otsuki

Jang

2022

ChemEngineering

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High energy consumption in size reduction operations is one of the most significant issues concerning the sustainability of raw material beneficiation. Thus, process optimization should be done to reduce energy consumption. This study aimed to investigate the applicability of artificial neural networks (ANNs) to predict the particle size distributions (PSDs) of mill products. PSD is one of the key sources of information after milling since it significantly affects the subsequent beneficiation processes. Thus, precise PSD prediction can contribute to process optimization and energy consumption reduction by avoiding over-grinding. In this study, coal particles (−2 mm) were ground with a rod mill under different conditions, and their PSDs were measured. The variables studied included volume% (vol.%) of feed (coal particle), vol.% rod load, and grinding time. Our supervised ANN models were developed to predict PSDs and trained by experimental data sets. The trained models were verified with the other experimental data sets. The results showed that the PSDs predicted by ANN fitted very well with the experimental data after the training. Root mean squared error (RMSE) was calculated for each milling condition, with results between 0.165 and 0.965. Also, the developed ANN models can predict the PSDs of ground products under different milling conditions (i.e., vol.% feed, vol.% rod load, and grinding time). The results confirmed the applicability of ANNs to predict PSD and, thus the potential contribution to reducing energy consumption by optimizing the grinding conditions.

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“…As a result, the experimental test and damage pattern were appropriately changed, increasing the CDPM stability in numerical calculations. It was found that column performance can be affected by changing the configurations instead of increasing the fabric layers [76].…”

Section: Strengthening Of Rc Column Using Frcmmentioning

confidence: 99%

A state of art of review on strengthening of concrete structures using fabric reinforced cementitious matrix

Sagare,

2024

Res. Eng. Struct. Mat.

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Renovation, refurbishment, restoration, and retrofitting of existing structures have grown more challenging issues for the construction profession. Construction buildings are susceptible to damage at the time of the earthquake and need strengthening to enhance their strength, stiffness, and ductility. Fabric-reinforced cementitious matrix (FRCM) techniques have been recently introduced to the construction sector as a feasible solution for fiber-reinforced polymers (FRP) in strengthening application. FRCM composed of high strength fabric grids with cement-based material serve as a binder for FRCM matrices. The binder employed for FRCM composed of cement-based mortar along with polymers are added to improve the bond strength properties. The cementitious matrix utilized in FRCM has superior thermal resistance and better compatibility with the concrete surface. The utilization of FRCM matrix for upgrading and restoring the concrete members is gaining more prominence as a replacement to FRP. This work conducts a thorough analysis of the application of FRCM techniques to concrete structural members like beams, columns, slabs, and beam-column joints. This paper primarily aims to present the FRCM process on structural members and to discuss the flexural, shear, and load carrying FRCM materials that are used in the field.

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Probabilistic assessment of axial load-carrying capacity of FRCM-strengthened concrete columns using artificial neural network and Monte Carlo simulation

Cited by 7 publications

References 34 publications

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

A state of art of review on strengthening of concrete structures using fabric reinforced cementitious matrix

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