Computational AI prediction models for residual tensile strength of GFRP bars aged in the alkaline concrete environment

Iqbal, Mudassir; Zhang, Daxu; Jalal, Fazal E.; Javed, Muhammad Faisal

doi:10.1016/j.oceaneng.2021.109134

Cited by 87 publications

(40 citation statements)

References 87 publications

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“…The paradigm shift in regression models using machine learning significantly contributes to solving engineering problems [ 67 , 68 , 69 , 70 , 71 ]. The current study investigated the effect of changing dosages of NGPs on the mechanical characteristics of concrete.…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of Cementitious Composites Incorporating Nano Graphite Platelets as Additive Carbon Material

et al. 2021

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Nano graphite platelets (NGPs) belong to the carbon family and have a huge impact on the construction industry. NGPs are used as multi-functional fillers and have the potential to develop reinforcing within cementitious composites. In this paper, NGPs were incorporated in cementitious composites to investigate the effects of NGPs on the fresh, mechanical, durability, and microstructural properties of concrete. Five mixes were prepared with intrusion of NGPs (0%, 0.5%, 1.5%, 3%, and 5% by weight of cement). The properties studied involved workability, air content, hardened density, compressive strength, tensile strength, flexural strength, sorptivity, ultrasonic pulse velocity (UPV), water absorption, and external sulfate attack. The workability and percent air content decrease by 22.5% and 33.8%, respectively, for concrete with 5% NGPs compared to the control mix. The specimens containing 5% of NGPs revealed the hardened density, compressive, tensile, and flexural strength to increase by 11.4%, 38.5%, 31.6%, and 44.34%, respectively, compared to the control mix. The results revealed that the incorporation of 5%NGPs in cementitious composites reduces the sorptivity and water absorption by 32.2% and 73.9%, respectively, whereas, it increases the UPV value by 7.5% compared to the control mix. Furthermore, the incorporation of NGPs provided better resistance against external sulfate attacks. SEM–EDX spectroscopy was carried out to investigate its microstructural analysis.

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Section: Discussionmentioning

confidence: 99%

Performance Evaluation of Cementitious Composites Incorporating Nano Graphite Platelets as Additive Carbon Material

et al. 2021

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“…Additionally, the use of plastic aggregate is recommended to be used in marine conditions owing to its non-absorption capacity, which resists the ingress of hazardous chemicals such as chloride and sulphate, etc. The conventional steel and fiber-reinforced polymer (FRP) rebars in PCA-incorporated concrete are expected to perform better under an alkaline environment; however, more insights regarding the durability of FRPs in PCA-incorporated concrete shall be investigated first from the relevant literature [ 59 , 60 ]. In addition, machine learning techniques are widely used for investigating material properties [ 61 , 62 , 63 , 64 , 65 , 66 , 67 ] and general engineering problems [ 68 , 69 ].…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of Plastic Concrete Modified with E-Waste Plastic as a Partial Replacement of Coarse Aggregate

et al. 2021

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Plastic electronic waste (E-waste) is constantly growing around the world owing to the rapid increase in industrialization, urbanization, and population. The current annual production rate of E-waste is 3–4% in the world and is expected to increase to 55 million tons per year by 2025. To reduce the detrimental impact on the environment and save natural resources, one of the best solutions is to incorporate waste plastic in the construction industry to produce green concrete. This study examines the use of manufactured plastic coarse aggregate (PCA) obtained from E-waste as a partial replacement of natural coarse aggregate (NCA) in concrete. Six types of concrete mix with 10%, 20%, 30%, 40%, and 50% substitution of NCA (by volume) with PCA are prepared and tested. This study investigates the effect of manufactured PCA on the fresh and hardened characteristics of concrete. The properties of recycled plastic aggregate concrete (RPAC) studied include workability, fresh density, dry density, compressive strength (CS), splitting tensile strength (STS), flexural strength (FS), sorptivity coefficient, abrasion resistance, ultrasonic pulse velocity (UPV), and alternate wetting and drying (W–D). The results indicate that the CS, STS, and FS of RPAC declined in the range of 9.9–52.7%, 7.8–47.5%, and 11–39.4%, respectively, for substitution ratios of 10–50%. However, the results also indicate that the incorporation of PCA (10–50%) improved the workability and durability characteristics of concrete. A significant decrement in the sorptivity coefficient, abrasion loss, and UPV value was observed with an increasing amount of PCA. Furthermore, RPAC containing different percentages of PCA revealed better results against alternate W–D cycles with respect to ordinary concrete.

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“…Many experimental and finite element studies have predicted both these capacities of RC columns with higher accuracy [ 1 , 11 , 14 ], but these traditional capacity determination techniques are costly, time-consuming, and laborious. The solution to the problem lies in the development of analytical models [ 13 , 15 , 16 , 17 , 18 , 19 , 20 ]. Up till now, many empirical models have been developed for the prediction of bearing capacity of RC columns, e.g., failure mode and bearing capacity prediction model [ 13 ], strut and tie model [ 21 , 22 ], modified compression field theory model [ 23 , 24 ], softened truss model [ 25 , 26 ], damage model [ 27 , 28 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…AI procedures are generally based on machine learning (ML), pattern recognition (PR) and deep learning (DL), which further consist of artificial neural networks (ANNs), fuzzy logic, genetic programming (GP), etc. [ 16 , 17 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The application of these AI techniques in structural engineering are traced back to the early 1980s, where they were first used in the compliance checking of design codes [ 51 , 52 ] and expert interactive design of concrete columns (EIDOCC) [ 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

et al. 2022

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This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

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Computational AI prediction models for residual tensile strength of GFRP bars aged in the alkaline concrete environment

Cited by 87 publications

References 87 publications

Performance Evaluation of Cementitious Composites Incorporating Nano Graphite Platelets as Additive Carbon Material

Performance Evaluation of Cementitious Composites Incorporating Nano Graphite Platelets as Additive Carbon Material

Performance Evaluation of Plastic Concrete Modified with E-Waste Plastic as a Partial Replacement of Coarse Aggregate

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

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