TY - JOUR AU - Javed, M.F. AU - Muhammad, Fawad AU - Lodhi, R. AU - Najeh, T. AU - Gamil, Y. TI - Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 14 PY - 2024 IS - 1 PG - 28 SN - 2045-2322 DO - 10.1038/s41598-024-57896-0 UR - https://m2.mtmt.hu/api/publication/34803148 ID - 34803148 N1 - Silesian University of Technology Poland, Gliwice, Poland Budapest University of Technology and Economics Hungary, Budapest, Hungary Department of Urban and Regional Planning, National University of Sciences and Technology (NUST), Islamabad, Pakistan Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Selangor, Bandar Sunway, 47500, Malaysia Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, 23640, Pakistan Export Date: 22 April 2024 Correspondence Address: Najeh, T.; Operation and Maintenance, Sweden; email: taoufik.najeh@ltu.se Correspondence Address: Javed, M.F.; Department of Civil Engineering, Pakistan; email: arbabfaisal@cuiatd.edu.pk Chemicals/CAS: water, 7732-18-5 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Alyami, M. AU - Nassar, R.-U.-D. AU - Khan, M. AU - Hammad, A.W. AU - Alabduljabbar, H. AU - Nawaz, R. AU - Muhammad, Fawad AU - Gamil, Y. TI - Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models JF - Case Studies in Construction Materials J2 - CASE STUD CONSTR MAT VL - 20 PY - 2024 PG - 24 SN - 2214-5095 DO - 10.1016/j.cscm.2024.e02901 UR - https://m2.mtmt.hu/api/publication/34742710 ID - 34742710 N1 - Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia Department of Civil and Infrastructure Engineering, American University of Ras Al Khaimah, United Arab Emirates Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan Principle Scientist, Macroview Projects, Sydney, Australia Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia Center for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology, Hawally, 32093, Kuwait Silesian University of Technology, Poland Budapest University of Technology and Economics, Hungary Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Selangor, Bandar Sunway, 47500, Malaysia Export Date: 18 March 2024 Correspondence Address: Khan, M.; Department of Civil Engineering, Pakistan; email: 18pwciv4988@uetpeshawar.edu.pk Correspondence Address: Gamil, Y.; Department of Civil, Sweden; email: yaser.gamil@ltu.se LA - English DB - MTMT ER - TY - JOUR AU - Alyousef, R. AU - Nassar, R.-U.-D. AU - Muhammad, Fawad AU - Farooq, F. AU - Gamil, Y. AU - Najeh, T. TI - Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches JF - Case Studies in Construction Materials J2 - CASE STUD CONSTR MAT VL - 20 PY - 2024 PG - 25 SN - 2214-5095 DO - 10.1016/j.cscm.2024.e03018 UR - https://m2.mtmt.hu/api/publication/34742500 ID - 34742500 N1 - Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia Department of Civil and Infrastructure Engineering, American University of Ras Al Khaimah, United Arab Emirates Silesian University of Technology Poland, Poland Budapest University of Technology and Economics Hungary, Hungary NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, 44000, Pakistan Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Selangor, Bandar Sunway, 47500, Malaysia Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Sweden Export Date: 18 March 2024 Correspondence Address: Alyousef, R.; Department of Civil Engineering, Saudi Arabia; email: r.alyousef@psau.edu.pk Funding details: Prince Sattam bin Abdulaziz University, PSAU, PSAU/2023/01/206862 Funding text 1: The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number ( PSAU/2023/01/206862 ). AB - 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 LA - English DB - MTMT ER - TY - JOUR AU - Király, Krisztián AU - Dunai, László TI - Experimental Study of Novel Demountable Shear Connectors for Steel-concrete Composite Buildings JF - PERIODICA POLYTECHNICA-CIVIL ENGINEERING J2 - PERIOD POLYTECH CIV ENG VL - 68 PY - 2024 IS - 2 SP - 647 EP - 656 PG - 10 SN - 0553-6626 DO - 10.3311/PPci.22732 UR - https://m2.mtmt.hu/api/publication/34727225 ID - 34727225 N1 - Export Date: 5 April 2024 Correspondence Address: Király, K.; Department of Structural Engineering, Műegyetem rkp. 3., Kmf. 85, Hungary; email: kiraly.krisztian@emk.bme.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Bärnkopf, Erzsébet AU - Kövesdi, Balázs Géza TI - Model factor for patch loading resistance of steel plated structures JF - POLLACK PERIODICA: AN INTERNATIONAL JOURNAL FOR ENGINEERING AND INFORMATION SCIENCES J2 - POLLACK PERIODICA PY - 2024 PG - 6 SN - 1788-1994 DO - 10.1556/606.2024.00981 UR - https://m2.mtmt.hu/api/publication/34726790 ID - 34726790 N1 - Export Date: 8 March 2024 Correspondence Address: Bärnkopf, E.; Department of Structural Engineering, Hungary; email: barnkopf.erzsebet@edu.bme.hu Funding details: Magyar Tudományos Akadémia, MTA Funding details: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFI, LP2021-06/2021 Funding details: Innovációs és Technológiai Minisztérium Funding text 1: The research was financially supported by the New National Excellence Programme; the Authors would like to thank the Ministry for Innovation and Technology and the National Research, Development and Innovation Office. The research work is also connected to the Grant MTA-BME Lendület LP2021-06/2021 “Theory of new generation steel bridges” program of the Hungarian Academy of Sciences; the financial support is gratefully acknowledged. AB - 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) LA - English DB - MTMT ER - TY - JOUR AU - Quillupangui, Irvin AU - Somodi, Balázs Norbert AU - Kövesdi, Balázs Géza TI - Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns JF - APPLIED SCIENCES-BASEL J2 - APPL SCI-BASEL VL - 14 PY - 2024 IS - 5 SP - 2029 SN - 2076-3417 DO - 10.3390/app14052029 UR - https://m2.mtmt.hu/api/publication/34722555 ID - 34722555 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Muhammad, Fawad AU - Marek, Salamak AU - Koris, Kálmán TI - Monitoring of the technical condition of an arch road bridge using BIM and AR technologies JF - E-BRIM VL - III. PY - 2024 IS - 01/2024 SP - 33 EP - 41 PG - 9 SN - 2788-0540 UR - https://m2.mtmt.hu/api/publication/34641502 ID - 34641502 LA - English DB - MTMT ER - TY - JOUR AU - Abdullah, G.M.S. AU - Ahmad, M. AU - Babur, M. AU - Badshah, M.U. AU - Al-Mansob, R.A. AU - Gamil, Y. AU - Muhammad, Fawad TI - Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 14 PY - 2024 IS - 1 PG - 15 SN - 2045-2322 DO - 10.1038/s41598-024-52825-7 UR - https://m2.mtmt.hu/api/publication/34568141 ID - 34568141 N1 - Department of Civil Engineering, College of Engineering, Najran University, P.O. 1988, Najran, Saudi Arabia Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, 43000, Malaysia Department of Civil Engineering, University of Engineering and Technology Peshawar, Bannu Campus, Bannu, 28100, Pakistan Department of Civil Engineering, Faculty of Engineering, University of Central Punjab, Lahore, 54000, Pakistan Water Wing, Water and Power Development Authority (WAPDA), WAPDA House Peshawar, Peshawar, 25000, Pakistan Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak, Selangor, 50728, Malaysia Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå, Sweden Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Selangor, Bandar Sunway, 47500, Malaysia Silesian University of Technology, Gliwice, Poland Budapest University of Technology and Economics, Budapest, Hungary Export Date: 9 February 2024 Correspondence Address: Ahmad, M.; Institute of Energy Infrastructure, Malaysia; email: ahmadm@uniten.edu.my Correspondence Address: Gamil, Y.; Department of Civil, Sweden; email: yaser.gamil@ltu.se Funding details: Najran University, NU, NU/NRP/SERC/12/12 Funding text 1: The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Priorities and Najran Research funding program Grant code (NU/NRP/SERC/12/12). AB - 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). LA - English DB - MTMT ER - TY - JOUR AU - Kövesdi, Balázs Géza AU - Kollár, Dénes AU - Dunai, László TI - Temporary Structural Health Monitoring of Historical Széchenyi Chain Bridge JF - BUILDINGS J2 - BUILDINGS-BASEL VL - 14 PY - 2024 IS - 2 PG - 15 SN - 2075-5309 DO - 10.3390/buildings14020535 UR - https://m2.mtmt.hu/api/publication/34561844 ID - 34561844 N1 - Export Date: 4 March 2024 Correspondence Address: Kövesdi, B.; Department of Structural Engineering, Műegyetem rkp. 3, Hungary; email: kovesdi.balazs@emk.bme.hu Funding details: Magyar Tudományos Akadémia, MTA, MTA-BME Lendület LP2021-06/2021 Funding text 1: This research was funded by the Hungarian Academy of Sciences, grant number MTA-BME Lendület LP2021-06/2021 “Theory of new generation steel bridges”. LA - English DB - MTMT ER - TY - JOUR AU - Muhammad, Fawad AU - Salamak, Marek AU - Hanif, Muhammad Usman AU - Koris, Kálmán AU - Ahsan, Muhammad AU - Rahman, Hadiya AU - Gerges, Michael AU - Salah, Mostafa Mohamed TI - Integration of Bridge Health Monitoring System With Augmented Reality Application Developed Using 3D Game Engine–Case Study JF - IEEE ACCESS J2 - IEEE ACCESS VL - 12 PY - 2024 SP - 16963 EP - 16974 PG - 12 SN - 2169-3536 DO - 10.1109/ACCESS.2024.3358843 UR - https://m2.mtmt.hu/api/publication/34561630 ID - 34561630 AB - In recent times, digital transformations and Industry 4.0 have revolutionized real-time bridge monitoring and its inspection. The use of smart Structural Health Monitoring (SHM) techniques is becoming powerful with the competencies of Building Information Modeling (BIM) tools, Artificial Intelligence (AI), Internet of Things (IoT), and Virtual/Augmented (VR/AR) technologies. However, the lack of interconnectivity between these tools limits their functionality. This research has addressed this problem by developing an integrated framework to assess serviceability and implement a smart SHM for a newly constructed extradosed bridge. Using Finite Element Analysis (FEA), the study proposes an integrated SHM system that utilizes various IoT sensors, including Wired Strain Gauges (WSG), Liquid Levelling Sensors (LLS), MEMS accelerometers, and a Weather Monitoring Station (WMS) to monitor concrete deformations, vertical displacements, structural vibrations, and weather conditions. BIM tool is used to develop the virtual replica of the proposed SHM system which is then used in the 3D Game Engine (GE) to develop an AR application. This application is then successfully deployed and tested in the AR headset (HoloLens) where its capabilities for onsite bridge health monitoring are discovered. This approach overcomes the limitations of HoloLens devices by providing real-time access to SHM data through a web platform, enabling on-site or remote AR-based bridge health monitoring. Conclusively, this paper emphasizes the numerical modeling of bridges for the design of a health monitoring system, that highlights the importance of robust SHM techniques in assessing bridge conditions. Moreover, it introduces a novel approach for smart bridge inspection and onsite visualization of structural defects in an AR environment. Authors LA - English DB - MTMT ER -