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For: Ben Chaabene W, Flah M, Nehdi ML. Machine learning prediction of mechanical properties of concrete: Critical review. Construction and Building Materials 2020;260:119889. [DOI: 10.1016/j.conbuildmat.2020.119889] [Cited by in Crossref: 66] [Cited by in F6Publishing: 16] [Article Influence: 33.0] [Reference Citation Analysis]
Number Citing Articles
1 Anjum M, Khan K, Ahmad W, Ahmad A, Amin MN, Nafees A. Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete. Polymers (Basel) 2022;14:3906. [PMID: 36146051 DOI: 10.3390/polym14183906] [Reference Citation Analysis]
2 Anjum M, Khan K, Ahmad W, Ahmad A, Amin MN, Nafees A. New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete. Materials 2022;15:6261. [DOI: 10.3390/ma15186261] [Reference Citation Analysis]
3 Le B, Vu V, Seo S, Tran B, Nguyen-sy T, Le M, Vu T. Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods. KSCE J Civ Eng. [DOI: 10.1007/s12205-022-1918-z] [Reference Citation Analysis]
4 Wang X, Liu H, Liu Y. Auto-tuning deep forest for shear stiffness prediction of headed stud connectors. Structures 2022;43:1463-77. [DOI: 10.1016/j.istruc.2022.07.054] [Reference Citation Analysis]
5 Munir MJ, Kazmi SMS, Wu Y, Lin X, Ahmad MR. Development of a novel compressive strength design equation for natural and recycled aggregate concrete through advanced computational modeling. Journal of Building Engineering 2022;55:104690. [DOI: 10.1016/j.jobe.2022.104690] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Jong S, Ong D, Oh E. A novel Bayesian inference method for predicting optimum strength gain in sustainable geomaterials for greener construction. Construction and Building Materials 2022;344:128255. [DOI: 10.1016/j.conbuildmat.2022.128255] [Reference Citation Analysis]
7 Khan K, Ahmad W, Amin MN, Ahmad A, Nazar S, Alabdullah AA. Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms. Polymers 2022;14:3065. [DOI: 10.3390/polym14153065] [Reference Citation Analysis]
8 Tapeh ATG, Naser MZ. Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices. Arch Computat Methods Eng. [DOI: 10.1007/s11831-022-09793-w] [Reference Citation Analysis]
9 Xu H, Chang R, Pan M, Li H, Liu S, Webber RJ, Zuo J, Dong N. Application of Artificial Neural Networks in Construction Management: A Scientometric Review. Buildings 2022;12:952. [DOI: 10.3390/buildings12070952] [Reference Citation Analysis]
10 Corral-bobadilla M, Lostado-lorza R, Somovilla-gómez F, Íñiguez-macedo S. Life cycle assessment multi-objective optimization for eco-efficient biodiesel production using waste cooking oil. Journal of Cleaner Production 2022;359:132113. [DOI: 10.1016/j.jclepro.2022.132113] [Reference Citation Analysis]
11 Zhang X, Akber MZ, Zheng W. Predicting the slump of industrially produced concrete using machine learning: A multiclass classification approach. Journal of Building Engineering 2022. [DOI: 10.1016/j.jobe.2022.104997] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Li S, Richard Liew J. Experimental and Data-Driven analysis on compressive strength of steel fibre reinforced high strength concrete and mortar at elevated temperature. Construction and Building Materials 2022;341:127845. [DOI: 10.1016/j.conbuildmat.2022.127845] [Reference Citation Analysis]
13 Degtyarev VV, Tsavdaridis KD. Buckling and ultimate load prediction models for perforated steel beams using machine learning algorithms. Journal of Building Engineering 2022;51:104316. [DOI: 10.1016/j.jobe.2022.104316] [Reference Citation Analysis]
14 Ahmed AHA, Jin W, Ali MAH. Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review. ACT 2022;20:404-29. [DOI: 10.3151/jact.20.404] [Reference Citation Analysis]
15 Shah SAR, Azab M, Seif Eldin HM, Barakat O, Anwar MK, Bashir Y. Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches. Buildings 2022;12:914. [DOI: 10.3390/buildings12070914] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Dabbaghi F, Tanhadoust A, Nehdi ML, Dehestani M, Yousefpour H. Investigation on optimal lightweight expanded clay aggregate concrete at high temperature based on deep neural network. Structural Concrete. [DOI: 10.1002/suco.202100694] [Reference Citation Analysis]
17 Khan K, Ahmad W, Amin MN, Ahmad A. A Systematic Review of the Research Development on the Application of Machine Learning for Concrete. Materials 2022;15:4512. [DOI: 10.3390/ma15134512] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
18 Dai L, Wu X, Zhou M, Ahmad W, Ali M, Sabri MMS, Salmi A, Ewais DYZ. Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete. Materials 2022;15:4450. [DOI: 10.3390/ma15134450] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Khan K, Ahmad W, Amin MN, Ahmad A, Nazar S, Al-Faiad MA. Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters. Polymers (Basel) 2022;14:2509. [PMID: 35746085 DOI: 10.3390/polym14122509] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Yang D, Zhao J, Suhail SA, Ahmad W, Kamiński P, Dyczko A, Salmi A, Mohamed A. Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence. Materials (Basel) 2022;15:4311. [PMID: 35744370 DOI: 10.3390/ma15124311] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
21 Zhang Z, Fidan I. Machine Learning-Based Void Percentage Analysis of Components Fabricated with the Low-Cost Metal Material Extrusion Process. Materials (Basel) 2022;15:4292. [PMID: 35744348 DOI: 10.3390/ma15124292] [Reference Citation Analysis]
22 Corral-bobadilla M, Lostado-lorza R, Somovilla-gómez F, Escribano-garcía R. Biosorption of Cu(II) ions as a method for the effective use of activated carbon from grape stalk waste: RMS optimization and kinetic studies. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2022;44:4706-26. [DOI: 10.1080/15567036.2022.2080891] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Zhang G, Ding Z, Wang Y, Fu G, Wang Y, Xie C, Zhang Y, Zhao X, Lu X, Wang X. Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber. Materials (Basel) 2022;15:4250. [PMID: 35744309 DOI: 10.3390/ma15124250] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Li Y, Zhang Q, Kamiński P, Deifalla AF, Sufian M, Dyczko A, Kahla NB, Atig M. Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques. Materials (Basel) 2022;15:4209. [PMID: 35744270 DOI: 10.3390/ma15124209] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Pan X, Xiao Y, Suhail SA, Ahmad W, Murali G, Salmi A, Mohamed A. Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients. Materials (Basel) 2022;15:4194. [PMID: 35744254 DOI: 10.3390/ma15124194] [Reference Citation Analysis]
26 Khan K, Ahmad W, Amin MN, Ahmad A, Nazar S, Alabdullah AA, Arab AMA. Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms. Materials (Basel) 2022;15:4108. [PMID: 35744167 DOI: 10.3390/ma15124108] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Li Z, Yoon J, Zhang R, Rajabipour F, Srubar Iii WV, Dabo I, Radlińska A. Machine learning in concrete science: applications, challenges, and best practices. npj Comput Mater 2022;8. [DOI: 10.1038/s41524-022-00810-x] [Reference Citation Analysis]
28 Ahmad A, Ahmad W, Aslam F, Joyklad P. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Studies in Construction Materials 2022;16:e00840. [DOI: 10.1016/j.cscm.2021.e00840] [Cited by in Crossref: 24] [Cited by in F6Publishing: 18] [Article Influence: 24.0] [Reference Citation Analysis]
29 Ekanayake I, Meddage D, Rathnayake U. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Studies in Construction Materials 2022;16:e01059. [DOI: 10.1016/j.cscm.2022.e01059] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Khan K, Ahmad A, Amin MN, Ahmad W, Nazar S, Arab AMA. Comparative Study of Experimental and Modeling of Fly Ash-Based Concrete. Materials (Basel) 2022;15:3762. [PMID: 35683062 DOI: 10.3390/ma15113762] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
31 Liu X, Wu Y, Zhou Y. Axial Compression Prediction and GUI Design for CCFST Column Using Machine Learning and Shapley Additive Explanation. Buildings 2022;12:698. [DOI: 10.3390/buildings12050698] [Reference Citation Analysis]
32 Amin MN, Khan K, Ahmad W, Javed MF, Qureshi HJ, Saleem MU, Qadir MG, Faraz MI. Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches. Polymers (Basel) 2022;14:2128. [PMID: 35632011 DOI: 10.3390/polym14102128] [Reference Citation Analysis]
33 Shen Z, Deifalla AF, Kamiński P, Dyczko A. Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning. Materials 2022;15:3523. [DOI: 10.3390/ma15103523] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
34 Park S, Fonseca JH, Marimuthu KP, Jeong C, Lee S, Lee H. Determination of material properties of bulk metallic glass using nanoindentation and artificial neural network. Intermetallics 2022;144:107492. [DOI: 10.1016/j.intermet.2022.107492] [Reference Citation Analysis]
35 Wakjira TG, Al-hamrani A, Ebead U, Alnahhal W. Shear capacity prediction of FRP-RC beams using single and ensenble ExPlainable Machine learning models. Composite Structures 2022;287:115381. [DOI: 10.1016/j.compstruct.2022.115381] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
36 Iftikhar B, Alih SC, Vafaei M, Elkotb MA, Shutaywi M, Javed MF, Deebani W, Khan MI, Aslam F. Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison. Journal of Cleaner Production 2022;348:131285. [DOI: 10.1016/j.jclepro.2022.131285] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
37 Linghu J, Dong H, Cui J. Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials. Comput Mech. [DOI: 10.1007/s00466-022-02170-1] [Reference Citation Analysis]
38 Han B, Wu Y, Liu L. Prediction and uncertainty quantification of compressive strength of high‐strength concrete using optimized machine learning algorithms. Structural Concrete. [DOI: 10.1002/suco.202100732] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
39 Bayerlein B, Hanke T, Muth T, Riedel J, Schilling M, Schweizer C, Skrotzki B, Todor A, Moreno Torres B, Unger JF, Völker C, Olbricht J. A Perspective on Digital Knowledge Representation in Materials Science and Engineering. Adv Eng Mater. [DOI: 10.1002/adem.202101176] [Reference Citation Analysis]
40 Thai H. Machine learning for structural engineering: A state-of-the-art review. Structures 2022;38:448-91. [DOI: 10.1016/j.istruc.2022.02.003] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 11.0] [Reference Citation Analysis]
41 Yu Y, Zhao XY, Xu JJ, Wang SC, Xie TY. Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models. Materials (Basel) 2022;15:2407. [PMID: 35407741 DOI: 10.3390/ma15072407] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
42 Todorov B, Muntasir Billah A. Machine learning driven seismic performance limit state identification for performance-based seismic design of bridge piers. Engineering Structures 2022;255:113919. [DOI: 10.1016/j.engstruct.2022.113919] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
43 Dabiri H, Kioumarsi M, Kheyroddin A, Kandiri A, Sartipi F. Compressive strength of concrete with recycled aggregate; a machine learning-based evaluation. Cleaner Materials 2022;3:100044. [DOI: 10.1016/j.clema.2022.100044] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
44 Razavi Setvati M, Hicks SJ. Machine learning models for predicting resistance of headed studs embedded in concrete. Engineering Structures 2022;254:113803. [DOI: 10.1016/j.engstruct.2021.113803] [Reference Citation Analysis]
45 Wakjira TG, Ibrahim M, Ebead U, Alam MS. Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM. Engineering Structures 2022;255:113903. [DOI: 10.1016/j.engstruct.2022.113903] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
46 Kina C, Turk K, Tanyildizi H. Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete. Structural Concrete. [DOI: 10.1002/suco.202100756] [Reference Citation Analysis]
47 Chen H, Deng T, Du T, Chen B, Skibniewski MJ, Zhang L. An RF and LSSVM–NSGA-II method for the multi-objective optimization of high-performance concrete durability. Cement and Concrete Composites 2022. [DOI: 10.1016/j.cemconcomp.2022.104446] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
48 Tanyildizi H, Marani A, Türk K, Nehdi ML. Hybrid deep learning model for concrete incorporating microencapsulated phase change materials. Construction and Building Materials 2022;319:126146. [DOI: 10.1016/j.conbuildmat.2021.126146] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
49 Shin HY, Lawrence C, Kota KR, Thamburaja P, Srinivasa A, Lacy TE, Reddy J. Experimental, theoretical and numerical studies on plain concrete fracture in the low-strain rate regime—A state-of-the-art review. Mechanics of Advanced Materials and Structures. [DOI: 10.1080/15376494.2021.2011501] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
50 Kumar A, Arora HC, Kumar K, Mohammed MA, Majumdar A, Khamaksorn A, Thinnukool O. Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach. Sustainability 2022;14:845. [DOI: 10.3390/su14020845] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
51 Li J, Yang Z, Qian G, Berto F. Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting. International Journal of Fatigue 2022. [DOI: 10.1016/j.ijfatigue.2022.106764] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
52 Peng Y, Unluer C. Analyzing the mechanical performance of fly ash-based geopolymer concrete with different machine learning techniques. Construction and Building Materials 2022;316:125785. [DOI: 10.1016/j.conbuildmat.2021.125785] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
53 Liang M, Chang Z, Wan Z, Gan Y, Schlangen E, Šavija B. Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete. Cement and Concrete Composites 2022;125:104295. [DOI: 10.1016/j.cemconcomp.2021.104295] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 10.0] [Reference Citation Analysis]
54 Eyo E, Abbey S, Lawrence T, Tetteh F. Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers. Geoscience Frontiers 2022;13:101296. [DOI: 10.1016/j.gsf.2021.101296] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
55 Tran N, Nguyen T, Phan V, Nguyen D, Zheludkevich M. A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel. Advances in Materials Science and Engineering 2021;2021:1-25. [DOI: 10.1155/2021/6967550] [Reference Citation Analysis]
56 Xu S, Wang Q, Lyu Y, Li Q, Reinhardt HW. Prediction of fracture parameters of concrete using an artificial neural network approach. Engineering Fracture Mechanics 2021;258:108090. [DOI: 10.1016/j.engfracmech.2021.108090] [Reference Citation Analysis]
57 Naser M, Kodur V, Thai H, Hawileh R, Abdalla J, Degtyarev VV. StructuresNet and FireNet: Benchmarking databases and machine learning algorithms in structural and fire engineering domains. Journal of Building Engineering 2021;44:102977. [DOI: 10.1016/j.jobe.2021.102977] [Cited by in Crossref: 12] [Cited by in F6Publishing: 6] [Article Influence: 12.0] [Reference Citation Analysis]
58 Nguyen T, Tran N, Nguyen D. Prediction of Axial Compression Capacity of Cold-Formed Steel Oval Hollow Section Columns Using ANN and ANFIS Models. Int J Steel Struct. [DOI: 10.1007/s13296-021-00557-z] [Reference Citation Analysis]
59 Wang X, Wu D, Zhang J, Yu R, Hou D, Shui Z. Design of sustainable ultra-high performance concrete: A review. Construction and Building Materials 2021;307:124643. [DOI: 10.1016/j.conbuildmat.2021.124643] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
60 Sun J, Ma Y, Li J, Zhang J, Ren Z, Wang X. Machine learning-aided design and prediction of cementitious composites containing graphite and slag powder. Journal of Building Engineering 2021;43:102544. [DOI: 10.1016/j.jobe.2021.102544] [Cited by in Crossref: 21] [Cited by in F6Publishing: 7] [Article Influence: 21.0] [Reference Citation Analysis]
61 Song H, Ahmad A, Farooq F, Ostrowski KA, Maślak M, Czarnecki S, Aslam F. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Construction and Building Materials 2021;308:125021. [DOI: 10.1016/j.conbuildmat.2021.125021] [Cited by in Crossref: 26] [Cited by in F6Publishing: 12] [Article Influence: 26.0] [Reference Citation Analysis]
62 Yin B, Liew K. Machine learning and materials informatics approaches for evaluating the interfacial properties of fiber-reinforced composites. Composite Structures 2021;273:114328. [DOI: 10.1016/j.compstruct.2021.114328] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
63 Dabbaghi F, Tanhadoust A, Nehdi M, Nasrollahpour S, Dehestani M, Yousefpour H. Life cycle assessment multi-objective optimization and deep belief network model for sustainable lightweight aggregate concrete. Journal of Cleaner Production 2021;318:128554. [DOI: 10.1016/j.jclepro.2021.128554] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 6.0] [Reference Citation Analysis]
64 Zhang X, Akber MZ, Zheng W. Prediction of seven-day compressive strength of field concrete. Construction and Building Materials 2021;305:124604. [DOI: 10.1016/j.conbuildmat.2021.124604] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
65 Wakjira TG, Alam MS, Ebead U. Plastic hinge length of rectangular RC columns using ensemble machine learning model. Engineering Structures 2021;244:112808. [DOI: 10.1016/j.engstruct.2021.112808] [Cited by in Crossref: 8] [Cited by in F6Publishing: 12] [Article Influence: 8.0] [Reference Citation Analysis]
66 Behnood A, Golafshani EM. Artificial Intelligence to Model the Performance of Concrete Mixtures and Elements: A Review. Arch Computat Methods Eng. [DOI: 10.1007/s11831-021-09644-0] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
67 Naser M. An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference. Automation in Construction 2021;129:103821. [DOI: 10.1016/j.autcon.2021.103821] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
68 Liu J, Huang L, Tian Z, Ye H. Knowledge-enhanced data-driven models for quantifying the effectiveness of PP fibers in spalling prevention of ultra-high performance concrete. Construction and Building Materials 2021;299:123946. [DOI: 10.1016/j.conbuildmat.2021.123946] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
69 Salami BA, Olayiwola T, Oyehan TA, Raji IA. Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach. Construction and Building Materials 2021;301:124152. [DOI: 10.1016/j.conbuildmat.2021.124152] [Cited by in Crossref: 8] [Cited by in F6Publishing: 13] [Article Influence: 8.0] [Reference Citation Analysis]
70 Hu X, Li B, Mo Y, Alselwi O. Progress in Artificial Intelligence-based Prediction of Concrete Performance. ACT 2021;19:924-36. [DOI: 10.3151/jact.19.924] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
71 Shah HA, Rehman SKU, Javed MF, Iftikhar Y. Prediction of compressive and splitting tensile strength of concrete with fly ash by using gene expression programming. Structural Concrete. [DOI: 10.1002/suco.202100213] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
72 Wang X, Liu Y, Xin H. Bond strength prediction of concrete-encased steel structures using hybrid machine learning method. Structures 2021;32:2279-92. [DOI: 10.1016/j.istruc.2021.04.018] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 12.0] [Reference Citation Analysis]
73 Sun J, Wang Y, Yao X, Ren Z, Zhang G, Zhang C, Chen X, Ma W, Wang X. Machine-Learning-Aided Prediction of Flexural Strength and ASR Expansion for Waste Glass Cementitious Composite. Applied Sciences 2021;11:6686. [DOI: 10.3390/app11156686] [Cited by in Crossref: 16] [Cited by in F6Publishing: 10] [Article Influence: 16.0] [Reference Citation Analysis]
74 Völker C, Firdous R, Stephan D, Kruschwitz S. Sequential learning to accelerate discovery of alkali-activated binders. J Mater Sci 2021;56:15859-81. [DOI: 10.1007/s10853-021-06324-z] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
75 Dabbaghi F, Rashidi M, Nehdi ML, Sadeghi H, Karimaei M, Rasekh H, Qaderi F. Experimental and Informational Modeling Study on Flexural Strength of Eco-Friendly Concrete Incorporating Coal Waste. Sustainability 2021;13:7506. [DOI: 10.3390/su13137506] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 7.0] [Reference Citation Analysis]
76 Gupta P, Gupta N, Saxena KK, Goyal S. Multilayer perceptron modelling of geopolymer composite incorporating fly ash and GGBS for prediction of compressive strength. Advances in Materials and Processing Technologies. [DOI: 10.1080/2374068x.2021.1946751] [Reference Citation Analysis]
77 Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research 2021;145:106449. [DOI: 10.1016/j.cemconres.2021.106449] [Cited by in Crossref: 31] [Cited by in F6Publishing: 50] [Article Influence: 31.0] [Reference Citation Analysis]
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