Abdeljaber O, Avci O, Inman DJ, “Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks”, Journal of sound and Vibration, 2016, 363, 33-53. https://doi.org/10.1016/j.jsv.2015.10.029
Abdollahzadeh G, Jahani E, Kashir Z, “Predicting of compressive strength of recycled aggregate concrete by genetic programming”, Computers and Concrete, 2016, 18, 155-163. https://doi.org/10.12989/cac.2016.18.2.155
Amani MA, Nasiri MM, “A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach”, Journal of Combinatorial Optimization, 2023, 45, 130. https://doi.org/10.1007/s10878-023-01057-y
Amani MA, Sarkodie SA, “Mitigating spread of contamination in meat supply chain management using deep learning”, Scientific Reports, 2022, 12, 5037. https://doi.org/10.1038/s41598-022-08993-5
Amani MA, Sarkodie SA, Shue JB, Nasiri MM, Tavakkoli-Moghaddam R, “A hybrid scenario-based robust model to design a relief logistics network: a data-driven approach”, 2023, 33, 1-41. http://dx.doi.org/10.2139/ssrn.4377160
Asselman A, Khaldi M, Aammou S, “Enhancing the prediction of student performance based on the machine learning XGBoost algorithm”, Interactive Learning Environments, 2023, 31, 3360-3379. https://doi.org/10.1080/10494820.2021.1928235
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. https://doi.org/10.1016/j.cemconres.2021.106449
Baduge SK, Thilakarathna S, Perera JS, Arashpour M, Sharafi P, Teodosio B, Shringi A, Mendis P, “Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications”, Automation in Construction, 2022, 141, 104440. https://doi.org/10.1016/j.autcon.2022.104440
Behnood A, Verian KP, Gharehveran MM, “Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength”, Construction and Building Materials, 2015, 98, 519-529. https://doi.org/10.1016/j.conbuildmat.2015.08.124
Chatterjee AK, Intelligent and Sustainable Cement Production: Transforming to Industry 4.0 Standards, CRC Press, 2015. https://doi.org/10.1201/9781003106791
Chopra P, Sharma RK, Kumar M, Chopra T, “Comparison of machine learning techniques for the prediction of compressive strength of concrete”, Advances in Civil Engineering, 2018. https://doi.org/10.1155/2018/5481705
Dhaliwal SS, Nahid A-A, Abbas R, “Effective intrusion detection system using XGBoost”, Information, 2018, 9, 149. https://doi.org/10.3390/info9070149
Dinesh A, Prasad BR, “Predictive models in machine learning for strength and life cycle assessment of concrete structures”, Automation in Construction, 2024, 162, 105412. https://doi.org/10.1016/j.autcon.2024.105412
Du B, Wei Q, Liu R, “An improved quantum-behaved particle swarm optimization for endmember extraction”, IEEE Transactions on Geoscience and Remote Sensing, 2019, 57, 6003-6017. https://doi.org/10.1109/TGRS.2019.2903875
Dutta D, Barai SV, “Prediction of compressive strength of concrete: machine learning approaches”, Recent Advances in Structural Engineering, Volume 1: Select Proceedings of SEC 2016, 2019, Springer, 503-513. https://doi.org/10.1007/978-981-13-0362-3_40
Feng D-C, Liu Z-T, Wang X-D, Chen Y, Chang J-Q, Wei D-F, Jiang Z-M, “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach”, Construction and Building Materials, 2020, 230, 117000. https://doi.org/10.1016/j.conbuildmat.2019.117000
Gad AG, “Particle swarm optimization algorithm and its applications: a systematic review”, Archives of Computational Methods in Engineering, 2022, 29, 2531-2561. https://doi.org/10.1007/s11831-021-09694-4
Gholampour A, Mansouri I, Kisi O, Ozbakkaloglu T, “Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models”, Neural Computing and Applications, 2020, 32, 295-308. https://doi.org/10.1007/s00521-018-3630-y
Golafshani EM, Behnood A, “Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete”, Applied Soft Computing, 2018, 64, 377-400. https://doi.org/10.1016/j.asoc.2017.12.030
Güçlüer K, Özbeyaz A, Göymen S, Günaydın O, “A comparative investigation using machine learning methods for concrete compressive strength estimation”, Materials Today Communications, 2021, 27, 102278.
https://doi.org/10.1016/j.mtcomm.2021.102278
Han Q, Gui C, Xu J, Lacidogna G, “A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm”, Construction and Building Materials, 2019, 226, 734-742. https://doi.org/10.1016/j.conbuildmat.2019.07.315
Iqbal MF, Liu Q-F, Azim I, Zhu X, Yang J, Javed MF, Rauf M, “Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming”, Journal of Hazardous Materials, 2020, 384, 121322. https://doi.org/10.1016/j.jhazmat.2019.121322
Jain M, Saihjpal V, Singh N, Singh SB, “An overview of variants and advancements of PSO algorithm”, Applied Sciences, 2022, 12, 8392. https://doi.org/10.3390/app12178392
Jalal M, Jalal H, “Retracted: Behavior assessment, regression analysis and support vector machine (SVM) modeling of waste tire rubberized concrete”, Elsevier, 2020. https://doi.org/10.1016/j.jclepro.2020.122960
Jiang X, Mahadevan S, Adeli H, “Bayesian wavelet packet denoising for structural system identification”, Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures, 2007, 14, 333-356. https://doi.org/10.1002/stc.161
Khademi F, Akbari M, Jamal SM, Nikoo M, “Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete”, Frontiers of Structural and Civil Engineering, 2017, 11, 90-99. https://doi.org/10.1007/s11709-016-0363-9
Khurshid K, Danish A, Salim MU, Bayram M, Ozbakkaloglu T, Mosaberpanah MA, “An in-depth survey demystifying the Internet of Things (IoT) in the construction industry: Unfolding new dimensions”, Sustainability, 2023, 15, 1275. https://doi.org/10.3390/su15021275
Kiangala SK, Wang Z, “An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment”, Machine Learning with Applications, 2021, 4, 100024. https://doi.org/10.1016/j.mlwa.2021.100024
Liu Q, Sun P, Fu X, Zhang J, Yang H, Gao H, Li Y, “Comparative analysis of BP neural network and RBF neural network in seismic performance evaluation of pier columns”, Mechanical Systems and Signal Processing, 2020, 141, 106707. https://doi.org/10.1016/j.ymssp.2020.106707
Mangalathu S, Jeon J-S, “Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques”, Engineering Structures, 2018, 160, 85-94. https://doi.org/10.1016/j.engstruct.2018.01.008
Moein MM, Saradar A, Rahmati K, Mousavinejad SHG, Bristow J, Aramali V, Karakouzian M, “Predictive models for concrete properties using machine learning and deep learning approaches: A review”, Journal of Building Engineering, 2023, 63, 105444. https://doi.org/10.1016/j.jobe.2022.105444
Mouratidis K, “Urban planning and quality of life: A reviw of pathways linking the built environment to subjective well-being”, Cities, 2021, 115, 103229. https://doi.org/10.1016/j.cities.2021.103229
Naderpour H, Poursaeidi O, Ahmadi M, “Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks”, Measurement, 2018, 126, 299-308. https://doi.org/10.1016/j.measurement.2018.05.051
Omran BA, Chen Q, Jin R, “Comparison of data mining techniques for predicting compressive strength of environmentally friendly concrete”, Journal of Computing in Civil Engineering, 2016, 30, 04016029. https://doi.org/10.1061/(ASCE)CP.1943-5487.000059
Quah TKN, Tay YWD, Lim JH, Tan MJ, Wong TN, LI KHH, “Concrete 3D printing: process parameters for process control, monitoring and diagnosis in automation and construction”, Mathematics, 2023, 11, 1499. https://doi.org/10.3390/math11061499
Salehi H, Burgueño R, “Emerging artificial intelligence methods in structural engineering”, Engineering Structures, 2018, 171, 170-189. https://doi.org/10.1016/j.engstruct.2018.05.084
Shi H-S, Xu B-W, Zhou X-C, “Influence of mineral admixtures on compressive strength, gas permeability and carbonation of high performance concrete”, Construction and Building Materials, 2009, 23, 1980-1985. https://doi.org/10.1016/j.conbuildmat.2008.08.021
Tahmouresi B, Nemati P, Asadi MA, Saradar A, Moein MM, “Mechanical strength and microstructure of engineered cementitious composites: A new configuration for direct tensile strength, experimental and numerical analysis”, Construction and Building Materials, 2021, 269, 121361. https://doi.org/10.1016/j.conbuildmat.2020.121361
Wang Z, Jin W, Dong Y, Frangopol DM, “Hierarchical life-cycle design of reinforced concrete structures incorporating durability, economic efficiency and green objectives”, Engineering Structures, 2018, 157, 119-131. https://doi.org/10.1016/j.engstruct.2017.11.022
Yeh I-C, “Analysis of strength of concrete using design of experiments and neural networks”, Journal of Materials in Civil Engineering, 2006, 18, 597-604. https://doi.org/10.1061/(ASCE)0899-1561(2006)18:4(597)
Zhang W, Zheng Q, Ashour A, Han B, “Self-healing cement concrete composites for resilient infrastructures: A review”, Composites Part B: Engineering, 2020, 189, 107892. https://doi.org/10.1016/j.compositesb.2020.107892