@article{MTMT:34839813, title = {Analysis of dynamic features on local fatigue cracks in steel bridges}, url = {https://m2.mtmt.hu/api/publication/34839813}, author = {Wei, Qingyang and Kövesdi, Balázs Géza and Cao, Maosen and Dunai, László}, doi = {10.1016/j.prostr.2024.03.028}, journal-iso = {PROCEDIA STRUCT INTEGRITY}, journal = {PROCEDIA STRUCTURAL INTEGRITY}, volume = {57}, unique-id = {34839813}, issn = {2452-3216}, year = {2024}, pages = {262-270}, orcid-numbers = {Kövesdi, Balázs Géza/0000-0002-0370-2820; Dunai, László/0000-0002-1018-2413} } @article{MTMT:34803148, title = {Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches}, url = {https://m2.mtmt.hu/api/publication/34803148}, author = {Javed, M.F. and Muhammad, Fawad and Lodhi, R. and Najeh, T. and Gamil, Y.}, doi = {10.1038/s41598-024-57896-0}, journal-iso = {SCI REP}, journal = {SCIENTIFIC REPORTS}, volume = {14}, unique-id = {34803148}, issn = {2045-2322}, abstract = {Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R2) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction. © The Author(s) 2024.}, keywords = {CAT; ARTICLE; PREDICTION; WATER; Sensitivity analysis; controlled study; Forecasting; Reliability; machine learning; computer interface; Sand; Compressive Strength; correlation coefficient; predictive model; cement; concrete; fly ash; random forest; Voting; Machine learning models; Mean absolute error; Construction engineering; root mean squared error; compressive strength prediction; Two-stage concrete; Preplaced aggregate concrete}, year = {2024}, eissn = {2045-2322} } @article{MTMT:34742710, title = {Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models}, url = {https://m2.mtmt.hu/api/publication/34742710}, author = {Alyami, M. and Nassar, R.-U.-D. and Khan, M. and Hammad, A.W. and Alabduljabbar, H. and Nawaz, R. and Muhammad, Fawad and Gamil, Y.}, doi = {10.1016/j.cscm.2024.e02901}, journal-iso = {CASE STUD CONSTR MAT}, journal = {Case Studies in Construction Materials}, volume = {20}, unique-id = {34742710}, issn = {2214-5095}, year = {2024}, eissn = {2214-5095} } @article{MTMT:34742500, title = {Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches}, url = {https://m2.mtmt.hu/api/publication/34742500}, author = {Alyousef, R. and Nassar, R.-U.-D. and Muhammad, Fawad and Farooq, F. and Gamil, Y. and Najeh, T.}, doi = {10.1016/j.cscm.2024.e03018}, journal-iso = {CASE STUD CONSTR MAT}, journal = {Case Studies in Construction Materials}, volume = {20}, unique-id = {34742500}, issn = {2214-5095}, abstract = {Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the durability and strength of the concrete. Nevertheless, the complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints in comprehensively capturing these intricate compositions. Thus, posing challenges in delivering precise and dependable estimations. Therefore, the current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS), in conjunction with experimental investigation to study the effect of the integration of graphene nanoplatelets (GNPs) on the electrical resistivity (ER) and compressive strength (CS) of concrete containing GNPs. Concrete containing GNPs demonstrated an improved fractional change in resistivity (FCR) and strength. The experimental measures depict that strength enhancement was notable at GNP concentrations of 0.05% and 0.1%, showcasing increases of 13.23% and 16.58%, respectively. Simultaneously, the highest observed FCR change reached −12.19% and −13%, respectively. The prediction efficacy of the three models proved to be outstanding in forecasting the characteristics of concrete containing GNPs. For CS, the GEP, ANN, and ANFIS models demonstrated impressive correlation coefficient (R) values of 0.974, 0.963, and 0.954, respectively. For electrical resistivity, the GEP, ANN, and ANFIS models exhibited high R-values of 0.999, 0.995, and 0.987, respectively. The comparative analysis of the models revealed that the GEP model delivered precise predictions for both ER and CS. The mean absolute error (MAE) of the GEP-CS model demonstrated a 14.51% reduction compared to the ANN-CS model and a substantial 48.15% improvement over the ANFIS-CS model. Similarly, the ANN-CS model displayed an MAE that was 38.14% lower compared to the ANFIS-CS model. Moreover, the MAE of the GEP-ER model demonstrated a 56.80% reduction compared to the ANN-CS model and a substantial 82.47% improvement over the ANFIS-CS model. The Shapley Additive explanation (SHAP) analysis provided that curing age exhibited the highest SHAP score. Thus, indicating its predominant contribution to CS prediction. In predicting ER, the graphene content exhibited the highest SHAP score, signifying its predominant contribution to ER estimation. This study highlights ML's accuracy in predicting the properties of concrete with graphene nanoplatelets, offering a fast and cost-effective alternative to time-consuming experiments. © 2024 The Authors}, keywords = {Regression Analysis; electric conductivity; nanostructured materials; Forecasting; Gene Expression; fuzzy systems; machine learning; machine learning; Graphene; nanomaterials; Compressive Strength; Machine-learning; Fuzzy inference; Durability; Predictive models; Predictive models; Multilayer neural networks; Fuzzy neural networks; Reinforced concrete; Graphene nanoplatelets; Neuro-fuzzy inference systems; STRENGTH MODELS; Shapley; Adaptive neuro-fuzzy inference; Gene-expression programming; SHAP analysis; Graphene nanoplatelets reinforced concrete; Graphene nanoplatelet reinforced concrete; Shapley additive explanation analyse}, year = {2024}, eissn = {2214-5095} } @article{MTMT:34727225, title = {Experimental Study of Novel Demountable Shear Connectors for Steel-concrete Composite Buildings}, url = {https://m2.mtmt.hu/api/publication/34727225}, author = {Király, Krisztián and Dunai, László}, doi = {10.3311/PPci.22732}, journal-iso = {PERIOD POLYTECH CIV ENG}, journal = {PERIODICA POLYTECHNICA-CIVIL ENGINEERING}, volume = {68}, unique-id = {34727225}, issn = {0553-6626}, abstract = {Sustainable composite structures in building construction are assembled using demountable structural elements that can be reused in the circular economy. The current research and development project, in cooperation with Budapest University of Technology and Economics and KeSZ Group, bim.GROUP Ltd., Hungary, aims to design a novel demountable steel-concrete composite slab and frame system for buildings. The key component of this construction is the demountable shear connector. In the current research, novel bolted shear connectors with embedded bolts and threaded rods are developed and studied that can fit the applied technology of the industrial partner. One of the leading aspects of this connection is the consideration of bolt hole clearance, since it occurs initial slip and stiffness reduction of the composite beam. In the first phase of the research program, demountable and economical structural details were developed, which can reduce the stiffness reduction with the proper resistance and ductility features. To study the behavior of these shear connections, a push-out experimental program was designed and completed in March and April 2023. It is observed that novel shear connectors have a proper behavior with sufficient resistance and ductility, which is applicable according to the Eurocode 4 standard and fits the objectives of the research and development project. In the paper, the developed structural details and the push-out experimental program are presented with general results and statements besides a detailed evaluation of a specified specimen type.}, keywords = {Shear Connector; sustainable structure; demountable composite structure}, year = {2024}, eissn = {1587-3773}, pages = {647-656}, orcid-numbers = {Dunai, László/0000-0002-1018-2413} } @article{MTMT:34726790, title = {Model factor for patch loading resistance of steel plated structures}, url = {https://m2.mtmt.hu/api/publication/34726790}, author = {Bärnkopf, Erzsébet and Kövesdi, Balázs Géza}, doi = {10.1556/606.2024.00981}, journal-iso = {POLLACK PERIODICA}, journal = {POLLACK PERIODICA: AN INTERNATIONAL JOURNAL FOR ENGINEERING AND INFORMATION SCIENCES}, unique-id = {34726790}, issn = {1788-1994}, abstract = {Direct resistance check by applying advanced numerical models is getting increasingly used for the design of steel slender plated structures. This method has to take into account the same uncertainties as traditional analytical design calculations and should ensure the Eurocode-based prescribed safety level. The application of the model factor gives the possibility to account for the model-related uncertainties. The current study focuses on the determination of the model factor for one specific failure mode, the patch loading resistance. Numerical model has been developed and validated based on laboratory test results. To evaluate the model uncertainties, physically possible modeling differences are introduced, and their effects are evaluated on the resistance. The final aim of the study is to determine the model factor for the analyzed girder type and failure mode based on statistical evaluation. © 2024 The Author(s)}, keywords = {numerical model; Patch loading resistance; model factor; plated structures}, year = {2024}, eissn = {1788-3911}, orcid-numbers = {Kövesdi, Balázs Géza/0000-0002-0370-2820} } @article{MTMT:34722555, title = {Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns}, url = {https://m2.mtmt.hu/api/publication/34722555}, author = {Quillupangui, Irvin and Somodi, Balázs Norbert and Kövesdi, Balázs Géza}, doi = {10.3390/app14052029}, journal-iso = {APPL SCI-BASEL}, journal = {APPLIED SCIENCES-BASEL}, volume = {14}, unique-id = {34722555}, abstract = {The local buckling behavior of welded square box section columns subjected to pure compression is investigated. Local buckling represents a crucial failure mode in thin-walled structures, exerting a significant impact on their overall stability and load bearing capacity. The primary objective of this research is to perform an extensive literature review considering the theoretical background of buckling phenomena and encompassing key findings and methodologies reported in previous studies. Additionally, the development and validation of a novel numerical model is presented, capable of accurately predicting the ultimate buckling capacity. Two different calculation methods are applied in the present study: (i) a numerical model using equivalent geometric imperfections to cover the residual stresses and out-of-straightness of plates, (ii) realistic geometric imperfections combined with an assumed residual stress pattern which has an experimental-based background. The objective of the numerical investigation is to investigate the accuracy of the numerical model by using different residual stress and imperfection patterns taken from the international literature. Many test results are collected from the international literature, to which the computational results are compared, and the effect of the residual stresses and geometric imperfections are analyzed. Based on the numerical analysis, the accuracy of the imperfection models is assessed and the imperfection model leading to the most accurate resistance is determined. The calculated buckling capacities are also compared to analytical design approaches, in which accuracy is also analyzed and evaluated. The current investigation proved the buckling curve developed by Schillo gives the most accurate results to the numerically calculated buckling resistance.}, year = {2024}, eissn = {2076-3417}, pages = {2029}, orcid-numbers = {Somodi, Balázs Norbert/0000-0001-5374-4650; Kövesdi, Balázs Géza/0000-0002-0370-2820} } @article{MTMT:34641502, title = {Monitoring of the technical condition of an arch road bridge using BIM and AR technologies}, url = {https://m2.mtmt.hu/api/publication/34641502}, author = {Muhammad, Fawad and Marek, Salamak and Koris, Kálmán}, journal = {E-BRIM}, volume = {III.}, unique-id = {34641502}, year = {2024}, eissn = {2788-0540}, pages = {33-41}, orcid-numbers = {Koris, Kálmán/0000-0002-6571-5555} } @article{MTMT:34568141, title = {Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil}, url = {https://m2.mtmt.hu/api/publication/34568141}, author = {Abdullah, G.M.S. and Ahmad, M. and Babur, M. and Badshah, M.U. and Al-Mansob, R.A. and Gamil, Y. and Muhammad, Fawad}, doi = {10.1038/s41598-024-52825-7}, journal-iso = {SCI REP}, journal = {SCIENTIFIC REPORTS}, volume = {14}, unique-id = {34568141}, issn = {2045-2322}, abstract = {The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS. © 2024, The Author(s).}, year = {2024}, eissn = {2045-2322} } @article{MTMT:34561844, title = {Temporary Structural Health Monitoring of Historical Széchenyi Chain Bridge}, url = {https://m2.mtmt.hu/api/publication/34561844}, author = {Kövesdi, Balázs Géza and Kollár, Dénes and Dunai, László}, doi = {10.3390/buildings14020535}, journal-iso = {BUILDINGS-BASEL}, journal = {BUILDINGS}, volume = {14}, unique-id = {34561844}, year = {2024}, eissn = {2075-5309}, orcid-numbers = {Kövesdi, Balázs Géza/0000-0002-0370-2820; Kollár, Dénes/0000-0002-0048-3327; Dunai, László/0000-0002-1018-2413} }