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Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2,   Pages 311-321 doi: 10.1007/s11709-019-0593-8

Abstract: system, and nearest neighbor models based on collection of 207 laboratory tests, are investigated for compressivestrength prediction of concrete at high temperature.Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressivestrength at high temperature.

Keywords： data driven model     compressive strength     concrete     high temperature    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: This study aims to improve the unconfined compressive strength of soils using additives as well as byTo predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratoryThe suggested models predicted the unconfined compressive strength of soils accurately and can be introducedresults, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressivestrength values of the investigated soils compared with the other parameters.

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressivestrength of concrete

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 1,   Pages 90-99 doi: 10.1007/s11709-016-0363-9

Abstract: Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete isacknowledged as the most ordinary method, however, it is very difficult to predict the compressive strengthstrength of concrete with 173 different mix designs.Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressivestrength prediction are carried out.

Keywords： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity    

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Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system

Ahmad SHARAFATI, H. NADERPOUR, Sinan Q. SALIH, E. ONYARI, Zaher Mundher YASEEN

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 1,   Pages 61-79 doi: 10.1007/s11709-020-0684-6

Abstract: Concrete compressive strength prediction is an essential process for material design and sustainabilitycolony, ANFIS–differential evolution (DE), and ANFIS–genetic algorithm to predict the foamed concrete compressivestrength.ANFIS–PSO– (C–O) yielded the best accurate prediction of compressive strength with an MP value of 0.96

Keywords： foamed concrete     adaptive neuro fuzzy inference system     nature-inspired algorithms     prediction of compressivestrength    

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Influence of accelerated curing on the compressive strength of polymer-modified concrete

Izhar AHMAD; Kashif Ali KHAN; Tahir AHMAD; Muhammad ALAM; Muhammad Tariq BASHIR

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 5,   Pages 589-599 doi: 10.1007/s11709-022-0789-1

Abstract: Furthermore, in designing concrete structures, compressive strength is the most significant of all parametersWhile 3-d and 7-d compressive strength reflects the strengths at early phases, the ultimate strengthThe compressive strength of EVA-modified concrete was studied by incorporating various concentrationsAn accelerated compressive strength at 3.5 hours was considered as a reference strength on the basisBased on the results of compressive strength test, it was concluded that the strength of EVA-modified

Keywords： compressive strength prediction     polymer-modified concrete     linear regression fit     early age strength    

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Empirical models and design codes in prediction of modulus of elasticity of concrete

Behnam VAKHSHOURI, Shami NEJADI

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 38-48 doi: 10.1007/s11709-018-0479-1

Abstract: It represents the stress-strain relationship in the elastic range and is used in the prediction of concreteincludes: (a) evaluation and comparison of the existing analytical models to estimating the MOE in normal strengthIn addition, a wide range of experimental databases and empirical models to estimate the MOE from compressivestrength and density of concrete are evaluated to verification of the proposed model.

Keywords： modulus of elasticity     normal strength normal weight concrete     empirical models     design codes     compressivestrength     density    

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Compressive strength prediction and optimization design of sustainable concrete based on squirrel search

Frontiers of Structural and Civil Engineering   Pages 1310-1325 doi: 10.1007/s11709-023-0997-3

Abstract: The complex relationship between influential factors and concrete compressive strength makes the predictionand estimation of compressive strength difficult.Instead of the traditional compressive strength test, this study combines five novel metaheuristic algorithmswith extreme gradient boosting (XGB) to predict the compressive strength of green concrete based onFinally, the developed SSA-XGB considered the effects of all the input factors on the compressive strength

Keywords： sustainable concrete     fly ash     slay     extreme gradient boosting technique     squirrel search algorithm     parametric analysis    

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Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressivestrength of concrete using rebound hammer field test

Dung Quang VU; Fazal E. JALAL; Mudassir IQBAL; Dam Duc NGUYEN; Duong Kien TRONG; Indra PRAKASH; Binh Thai PHAM

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 1003-1016 doi: 10.1007/s11709-022-0846-9

Abstract: These were used to predict compressive strength (Cs) of concrete relating to thirteen concrete-strengthreflected better convergence of the results and better performance compared to that of ANFIS-SCE in the predictionThus, the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of

Keywords： shuffled complex evolution     artificial bee colony     ANFIS     concrete     compressive strength     Vietnam    

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The ITZ microstructure, thickness, porosity and its relation with compressive and flexural strength of

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 191-201 doi: 10.1007/s11709-021-0792-y

Abstract: morphology and thickness is elucidated by backscattered electron images and by consequences to the compressive(Fc) and flexural strength (Ff), and porosity at various water/cementThese findings illustrate that the influence of ITZ features on the mechanical strength of CMSs is mostly

Keywords： cement fineness     interfacial transition zone     compressive and flexural strength    

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Optimizing the compressive strength of concrete containing micro-silica, nano-silica, and polypropylene

Fatemeh ZAHIRI, Hamid ESKANDARI-NADDAF

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 4,   Pages 821-830 doi: 10.1007/s11709-019-0518-6

Abstract: , micro-silica (MS) and polymer fibers on optimizing the mechanical properties of concrete, such as compressivestrength.Results indicated the sensitivity of each CSCs can be different on the NS or MS in compressive strengthConsequently, strength classes have a significant effect on the amount of MS and NS in mix design ofWhile, polymer fibers don’t have significant effect in compressive strength considering CSCs.

Keywords： mixture method     compressive strength     nano-silica     micro-silica     polypropylene fibers    

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 284-305 doi: 10.1007/s11709-022-0901-6

Abstract: Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressivestrength (CS) is a critical mechanical property that must be adequately determined.

Keywords： compressive strength     self-compacting concrete     artificial neural network     decision tree     CatBoost    

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Effect of calcium lactate on compressive strength and self-healing of cracks in microbial concrete

Kunamineni VIJAY, Meena MURMU

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3,   Pages 515-525 doi: 10.1007/s11709-018-0494-2

Abstract: This paper presents the effect on compressive strength and self-healing capability of bacterial concreteThe influence of this addition on compressive strength, self-healing capability of cracks is highlightedA maximum of 12% increase in compressive strength was observed with the addition of 0.5% of calcium lactateA statistical technique was applied to analyze the experimental data of the compressive strengths ofstrength and self-healing properties of concrete.

Keywords： calcium lactate     bacillus subtilis     compressive strength     self-healing of cracks    

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Enhancing compressive strength and durability of self-compacting concrete modified with controlled-burnt

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 161-174 doi: 10.1007/s11709-021-0796-7

Abstract: showed that the presence of amorphous silica was detected for the processed SBA, revealing that the strengthThe compressive strength of SCC containing SBA (without BFS) could reach 98%−127% of that of the control; combination of SBA and 30% BFS gets a similar strength to the control after 28 d.Finally, a hyperbolic formula for interpolating the compressive strength of the SBA-based SCC was proposed

Keywords： sugarcane bagasse ash     self-compacting concrete     compressive strength     sulfate resistance     water absorption     strength formula    

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Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariate

Ali Reza GHANIZADEH, Morteza RAHROVAN

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 4,   Pages 787-799 doi: 10.1007/s11709-019-0516-8

Abstract: For design and quality control of the final product in FDR method, the unconfined compressive strengthThis paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCSThe results demonstrate that MARS has a great ability for prediction of the UCS in case of soil-RAP blend

Keywords： full-depth reclamation     soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 215-239 doi: 10.1007/s11709-018-0489-z

Abstract: strength of bentonite/sepiolite plastic concretes.For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plasticThe results confirm the ability of ANN and SVM models in prediction processes.strength, respectively.Finally, the influence of different variables on the plastic concrete compressive strength values was

Keywords： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine    

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Title Author Date Type Operation

Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressivestrength of concrete

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Journal Article

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system

Ahmad SHARAFATI, H. NADERPOUR, Sinan Q. SALIH, E. ONYARI, Zaher Mundher YASEEN

Journal Article

Influence of accelerated curing on the compressive strength of polymer-modified concrete

Izhar AHMAD; Kashif Ali KHAN; Tahir AHMAD; Muhammad ALAM; Muhammad Tariq BASHIR

Journal Article

Empirical models and design codes in prediction of modulus of elasticity of concrete

Behnam VAKHSHOURI, Shami NEJADI

Journal Article

Compressive strength prediction and optimization design of sustainable concrete based on squirrel search

Journal Article

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressivestrength of concrete using rebound hammer field test

Dung Quang VU; Fazal E. JALAL; Mudassir IQBAL; Dam Duc NGUYEN; Duong Kien TRONG; Indra PRAKASH; Binh Thai PHAM

Journal Article

The ITZ microstructure, thickness, porosity and its relation with compressive and flexural strength of

Journal Article

Optimizing the compressive strength of concrete containing micro-silica, nano-silica, and polypropylene

Fatemeh ZAHIRI, Hamid ESKANDARI-NADDAF

Journal Article

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Journal Article

Effect of calcium lactate on compressive strength and self-healing of cracks in microbial concrete

Kunamineni VIJAY, Meena MURMU

Journal Article

Enhancing compressive strength and durability of self-compacting concrete modified with controlled-burnt

Journal Article

Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariate

Ali Reza GHANIZADEH, Morteza RAHROVAN

Journal Article

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Journal Article