Artificial Neural Network Model for Predicting the Tendon Stress in Unbonded Posttensioned Concrete Members at the Ultimate Limit State Torgeir Selsøyvold, Samindi M. K. Samarakoon, Piotr Nazarko Journal of Structural Engineering United States, 2022 Existing design guidelines, codes, and literature provide different calculation models for the estimation of tendon stresses in unbonded posttensioned concrete members at the ultimate limit state. Most of these methods are based on theoretical (e.g., collapse mechanism and bond-reduction models) and statistically-based empirical models, with only a few or no surrogate models based on artificial neural networks (ANNs). This study presents an ANN-based model to predict stress in unbonded tendons at the ultimate limit state based on a database of 251 prestressed concrete members with unbonded tendons collected from the literature. The predictions from the ANN-based model show very good agreement with the experimental results given in the literature during training, testing, and validation. A sensitivity analysis has been performed to quantify the degree of influence of the input variables used in the developed ANN model. The analysis shows that the predictions of tendon stress using neural networks are more accurate than those results obtained using the models given in the design guidelines and the literature.
Application of elastic waves and neural networks for the prediction of forces in bolts of flange connections subjected to static tension tests Piotr Nazarko, Leonard Ziemiański Materials, 2020 There is a group of measurement techniques that can be used in the task of force identification in steel bolts. In this paper, the potential of elastic wave propagation signals was studied for possible application in force monitoring systems. A series of laboratory tests was carried out on flange connections subjected to static tensile tests. Each one contained six screws of the same diameter. Four bolts were equipped with washer load cells. Alternatively, selected bolts were equipped with piezoelectric transducers (actuator and sensor) in order to measure the elastic wave signals. Principal components analysis, time of arrival, and neural network compression were used for dimensionality reduction of the measured signals. Examples of the obtained results with respect to the studied connections show that the tension forces in bolts can be estimated with relatively good accuracy.
Anomaly detection in composite elements using Lamb waves and soft computing methods Piotr Nazarko, Leonard Ziemiański Procedia Structural Integrity, 2017 Composite materials are widely used in many important structures, which in turn entails the need to develop sensitive and reliable structural health monitoring (SHM) systems. The aim of this study was to investigate the use of guided waves and artificial neural networks as essential components of a two-stage diagnostics system. This system was designed to detect anomalies and to assess their parameters. This paper presents the first result of the application of this system for evaluation of samples made from composite materials. Defects of various origin were artificially introduced. Grids of 8 and 12 piezoelectric transducers were used. Principal components analysis was used for dimensionality reduction of measured signals. Examples of preliminary fault detection results showed that any signal anomalies are detected perfectly whereas the prediction of damage level allowed to distinguishing the defects. Successful experiments carried out on the studied specimens have already proved that this system was able to perform automatic analysis of the elastic waves and accelerate the process of structures diagnosis.
Force identification in bolts of flange connections for structural health monitoring and failure prevention Piotr Nazarko, Leonard Ziemianski Procedia Structural Integrity, 2017 Abstract Force identification in bolts of flange connection is not only important to preserve the structure integrity but also to understand how does it works or even improve code procedures. Due to the relaxation phenomenon it becomes even more important in case of compressed bolts. In this paper a bolted flange connection was examined during static tensile test. Four of six bolts were equipped with washer load cells. Alternatively some bolts were equipped with piezoelectric transducers (actuator and sensor) in order to measure signals of elastic waves. It was noted that the load increasing causes changes in the signals measured. Principal components analysis was used for dimensionality reduction of measured signals. The aim of this study was to investigate the use of elastic waves and artificial neural networks for the purpose of force identification. Examples of preliminary results have shown that force in each bolt may be estimated with relatively good accuracy.
Novelty detection based on elastic wave signals measured by different techniques Computer Assisted Methods in Engineering and Science, 2012
Application of artificial neural networks in the damage identification of structural elements Computer Assisted Mechanics and Engineering Sciences, 2011
Laboratory tests on elastic waves application to damage detection in metal, plexiglass strips and composite plates Proceedings of the 4th European Workshop on Structural Health Monitoring, 2008
Failure identification in steel structure members based on wave propagation Proceedings of the 11th International Conference on Metal Structures Icms 2006 Progress in Steel Composite and Aluminium Structures, 2006
RECENT SCHOLAR PUBLICATIONS
Deep Learning Models for the Recognition of Building Elements in Residential Floor Plan Drawings N Bróż, P Nazarko, A Prokop, A Borowiec, R Lichołai International Conference Current Issues of Civil and Environmental … , 2025 2025 Citations: 1
Application of machine learning classifiers to elastic wave signal analysis and diagnostics of bolted joints N Bróż, P Nazarko Materiały Budowlane , 2025 2025
Variant analysis of the spatial bearing capacity of a lattice girder the load-bearing structure of the membrane roof of the amphitheater N Bróż, P Nazarko, A Prokop, R Lichołai Materiały Budowlane , 2025 2025
Proceedings of CEE 2023: Civil and Environmental Engineering and Architecture Z Blikharskyy, P Koszelnik, L Lichołai, P Nazarko, D Katunský Springer , 2024 2024 Citations: 2
Springer: Cham Z Blikharskyy, P Koszelnik, L Lichołai, P Nazarko, D Katunský Switzerland , 2024 2024 Citations: 6
Challenges in assessing the vibrations influence on people in buildings using non-contact measurements N Bróż, P Nazarko Journal of Civil Engineering, Environment and Architecture 70, 51-64 , 2023 2023
based technology education–the EDURES partnership experience R Wdowik, M Magdziak, A Dzierwa, B Ciecińska, P Podulka, J Litwin, ... Technologia i Automatyzacja Montażu (Assembly Techniques and Technologies … , 2023 2023
Artificial neural network model for predicting the tendon stress in unbonded posttensioned concrete members at the ultimate limit state T Selsøyvold, SMK Samarakoon, P Nazarko Journal of Structural Engineering 148 (10), 04022151 , 2022 2022 Citations: 5
Didactic guide for utilization of digital manufacturing tools for product development and manufacturing (ProDeM) in higher education: guidebook R Wdowik, M Magdzik, P Nazarko, R Śliwa, A Bełzo, R Bendikienė, ... Oficyna Wydawnicza Politechniki Rzeszowskiej , 2022 2022
SHM system for anomaly detection of bolted joints in engineering structures D Ziaja, P Nazarko Structures 33, 3877-3884 , 2021 2021 Citations: 33
Digitalization of historic buildings using modern technologies and tools A Prokop, P Nazarko, L Ziemiański Budownictwo i Architektura 20 (2) , 2021 2021 Citations: 13
Application of elastic waves and neural networks for the prediction of forces in bolts of flange connections subjected to static tension tests P Nazarko, L Ziemiański Materials 13 (16), 3607 , 2020 2020 Citations: 16
Analiza statyczno-wytrzymałościowa modelu MES istniejącej hali z wykorzystaniem skaningu laserowego D Ziaja, S Rachwał, P Nazarko Inżynieria i Budownictwo 76 , 2020 2020 Citations: 1
Anomaly detection in the concrete arc girder subjected to fatigue test D Ziaja, P Nazarko MATEC Web of Conferences 285, 00025 , 2019 2019 Citations: 3
Axial force prediction based on signals of the elastic wave propagation and artificial neural networks P Nazarko MATEC Web of Conferences 262, 10009 , 2019 2019 Citations: 10
Analiza możliwości zastosowania sztucznych sieci neuronowych do kalibracji modeli mikrosymulacyjnych M Szarata, P Nazarko Czasopismo Inżynierii Lądowej, Środowiska i Architektury/Journal of Civil … , 2017 2017 Citations: 1
Anomaly detection in composite elements using Lamb waves and soft computing methods P Nazarko, L Ziemiański Procedia Structural Integrity 5, 131-138 , 2017 2017 Citations: 5
Force identification in bolts of flange connections for structural health monitoring and failure prevention P Nazarko, L Ziemianski Procedia Structural Integrity 5, 460-467 , 2017 2017 Citations: 41
Damage detection in aluminum and composite elements using neural networks for Lamb waves signal processing P Nazarko, L Ziemianski Engineering Failure Analysis 69, 97-107 , 2016 2016 Citations: 46
Analysis of Lamb wave dispersion curve sensitivity to material elastic constants in composites A Gallina, L Pieczonka, L Ambrozinski, P Packo, P Nazarko, T Uhl, ... Health Monitoring of Structural and Biological Systems 2015 9438, 171-177 , 2015 2015 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
Application of neural networks for the structural health monitoring in curtain-wall systems C Efstathiades, CC Baniotopoulos, P Nazarko, L Ziemianski, ... Engineering Structures 29 (12), 3475-3484 , 2007 2007 Citations: 70
Damage detection in aluminum and composite elements using neural networks for Lamb waves signal processing P Nazarko, L Ziemianski Engineering Failure Analysis 69, 97-107 , 2016 2016 Citations: 46
Force identification in bolts of flange connections for structural health monitoring and failure prevention P Nazarko, L Ziemianski Procedia Structural Integrity 5, 460-467 , 2017 2017 Citations: 41
SHM system for anomaly detection of bolted joints in engineering structures D Ziaja, P Nazarko Structures 33, 3877-3884 , 2021 2021 Citations: 33
Application of artificial neural networks in the damage identification of structural elements P Nazarko, L Ziemiański Computer Assisted Methods in Engineering and Science 18 (3), 175-189 , 2011 2011 Citations: 33
Application of elastic waves and neural networks for the prediction of forces in bolts of flange connections subjected to static tension tests P Nazarko, L Ziemiański Materials 13 (16), 3607 , 2020 2020 Citations: 16
Digitalization of historic buildings using modern technologies and tools A Prokop, P Nazarko, L Ziemiański Budownictwo i Architektura 20 (2) , 2021 2021 Citations: 13
Axial force prediction based on signals of the elastic wave propagation and artificial neural networks P Nazarko MATEC Web of Conferences 262, 10009 , 2019 2019 Citations: 10
Soft computing methods in the analysis of elastic wave signals and damage identification P Nazarko Inverse Problems in Science and Engineering 21 (6), 945-956 , 2013 2013 Citations: 10
Application of the elastic waves and neural networks as a tool of damage detection and health monitoring in aircraft's structures P Nazarko, L Ziemiański Procedia Engineering 114, 393-400 , 2015 2015 Citations: 9
Towards application of soft computing in structural health monitoring P Nazarko, L Ziemiański International Conference on Artificial Intelligence and Soft Computing, 56-63 , 2010 2010 Citations: 8
Springer: Cham Z Blikharskyy, P Koszelnik, L Lichołai, P Nazarko, D Katunský Switzerland , 2024 2024 Citations: 6
Artificial neural network model for predicting the tendon stress in unbonded posttensioned concrete members at the ultimate limit state T Selsøyvold, SMK Samarakoon, P Nazarko Journal of Structural Engineering 148 (10), 04022151 , 2022 2022 Citations: 5
Anomaly detection in composite elements using Lamb waves and soft computing methods P Nazarko, L Ziemiański Procedia Structural Integrity 5, 131-138 , 2017 2017 Citations: 5
Application of eddy current sensor system and LDV device for ultrasonic vibrations measurements R Wdowik, P Nazarko, J Porzycki Mechatronics-Ideas for Industrial Application, 407-415 , 2015 2015 Citations: 5
Ocena stanu konstrukcji: detekcja uszkodzeĹ „z zastosowaniem sztucznych sieci neuronowych P Nazarko Oficyna Wydawnicza Politechniki Rzeszowskiej , 2010 2010 Citations: 4
Anomaly detection in the concrete arc girder subjected to fatigue test D Ziaja, P Nazarko MATEC Web of Conferences 285, 00025 , 2019 2019 Citations: 3
Novelty detection based on elastic wave signals measured by different techniques P Nazarko, L Ziemiański Computer Assisted Methods in Engineering and Science 19 (4), 317-330 , 2012 2012 Citations: 3
Proceedings of CEE 2023: Civil and Environmental Engineering and Architecture Z Blikharskyy, P Koszelnik, L Lichołai, P Nazarko, D Katunský Springer , 2024 2024 Citations: 2
Analysis of Lamb wave dispersion curve sensitivity to material elastic constants in composites A Gallina, L Pieczonka, L Ambrozinski, P Packo, P Nazarko, T Uhl, ... Health Monitoring of Structural and Biological Systems 2015 9438, 171-177 , 2015 2015 Citations: 2
