{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:23:55Z","timestamp":1772821435231,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T00:00:00Z","timestamp":1588118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014434","name":"Polish National Agency for Academic Exchange","doi-asserted-by":"publisher","award":["PPI\/APM\/2018\/1\/00004"],"award-info":[{"award-number":["PPI\/APM\/2018\/1\/00004"]}],"id":[{"id":"10.13039\/501100014434","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Operational Load Monitoring consists of the real-time reading and recording of the number and level of strains and stresses during load cycles withstood by a structure in its normal operating environment, in order to make more reliable predictions about its remaining lifetime in service. This is particularly important in aeronautical and aerospace industries, where it is very relevant to extend the components useful life without compromising flight safety. Sensors, like strain gauges, should be mounted on points of the structure where highest strains or stresses are expected. However, if the structure in its normal operating environment is subjected to variable exciting forces acting in different points over time, the number of places where data will have be acquired largely increases. The main idea presented in this paper is that instead of mounting a high number of sensors, an artificial neural network can be trained on the base of finite element simulations in order to estimate the state of the structure in its most stressed points based on data acquired just by a few sensors. The model should also be validated using experimental data to confirm proper predictions of the artificial neural network. An example with an omega-stiffened composite structural panel (a typical part used in aerospace applications) is provided. Artificial neural network was trained using a high-accuracy finite element model of the structure to process data from six strain gauges and return information about the state of the panel during different load cases. The trained neural network was tested in an experimental stand and the measurements confirmed the usefulness of presented approach.<\/jats:p>","DOI":"10.3390\/s20092534","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T13:23:45Z","timestamp":1588166625000},"page":"2534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Operational Load Monitoring of a Composite Panel Using Artificial Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9724-1817","authenticated-orcid":false,"given":"Waldemar","family":"Mucha","sequence":"first","affiliation":[{"name":"Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wac\u0142aw","family":"Ku\u015b","sequence":"additional","affiliation":[{"name":"Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00falio C.","family":"Viana","sequence":"additional","affiliation":[{"name":"Department of Polymer Engineering, IPC\u2014Institute for Polymers and Composites, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo\u00e3o Pedro","family":"Nunes","sequence":"additional","affiliation":[{"name":"Department of Polymer Engineering, IPC\u2014Institute for Polymers and Composites, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1111\/j.1475-1305.2000.tb01187.x","article-title":"Operational Load Monitoring for Aircraft & Maritime Applications","volume":"36","author":"Aldridge","year":"2000","journal-title":"Strain"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.autcon.2018.09.024","article-title":"Data-driven vision-based inspection for reinforced concrete beams and slabs: Quantitative damage and load estimation","volume":"96","author":"Davoudi","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1177\/1475921704047500","article-title":"Vibration Based Condition Monitoring: A Review","volume":"3","author":"Carden","year":"2004","journal-title":"Struct. Health Monit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"053001","DOI":"10.1088\/0964-1726\/25\/5\/053001","article-title":"Guided wave based structural health monitoring: A review","volume":"25","author":"Mitra","year":"2016","journal-title":"Smart Mater. Struct."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1177\/1475921710365419","article-title":"Vibration-based Damage Identification Methods: A Review and Comparative Study","volume":"10","author":"Fan","year":"2011","journal-title":"Struct. Health Monit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1109\/JLT.2013.2263278","article-title":"Echelle Diffractive Grating Based Wavelength Interrogator for Potential Aerospace Applications","volume":"31","author":"Guo","year":"2013","journal-title":"J. Lightwave Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kurnyta, A., Zielinski, W., Reymer, P., and Dziendzikowski, M. (2017). Operational Load Monitoring System Implementation for Su-22UM3K Aging Aircraft. Structural Health Monitoring 2017 Real-Time Material State Awareness and Data-Driven Safety Assurance Proceedings of the Eleventh International Workshop on Structural Health Monitoring, DEStech Publications, Inc.","DOI":"10.12783\/shm2017\/13856"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Giurgiutiu, V. (2016). Chapter 10\u2014SHM of Fatigue Degradation and Other In-Service Damage of Aerospace Composites. Structural Health Monitoring of Aerospace Composites, Academic Press.","DOI":"10.1016\/B978-0-12-409605-9.00010-6"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bos, M.J. (2009). Olm: A Hands-on Approach. ICAF 2009, Bridging the Gap Between Theory and Operational Practice, Proceedings of the 25th Symposium of the International Committee on Aeronautical Fatigue, Rotterdam, the Netherlands, 27\u201329 May 2009, Springe.","DOI":"10.1007\/978-90-481-2746-7"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yasin, M., Harun, S.W., and Arof, H. (2012). Intrinsic Optical Fiber Sensor. Fiber Optic Sensors, IntechOpen.","DOI":"10.5772\/1379"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1243\/09544100JAERO34","article-title":"Multi-functional fiber Bragg grating sensors for fatigue crack detection in metallic structures","volume":"220","author":"Betz","year":"2006","journal-title":"Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/JLT.2005.862442","article-title":"Staszewski Advanced layout of a fiber Bragg grating strain gauge rosette","volume":"24","author":"Betz","year":"2006","journal-title":"J. Lightwave Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3687","DOI":"10.3390\/s110403687","article-title":"Fiber Optic Sensors for Structural Health Monitoring of Air Platforms","volume":"11","author":"Guo","year":"2011","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Timofiejczuk, A., \u0141azarz, B.E., Chaari, F., and Burdzik, R. (2018). Memetic Inverse Problem Solution in Cyber-Physical Systems. Advances in Technical Diagnostics, Springer.","DOI":"10.1007\/978-3-319-62042-8"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.acme.2012.11.004","article-title":"Springback prediction in T-section beam bending process using neural networks and finite element method","volume":"13","author":"Song","year":"2013","journal-title":"Arch. Civil Mech. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.dt.2014.12.001","article-title":"Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools","volume":"11","author":"Ekici","year":"2015","journal-title":"Defence Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Burczy\u0144ski, T., Ku\u015b, W., Beluch, W., D\u0142ugosz, A., Poteralski, A., and Szczepanik, M. (2020). Intelligent Computing in Inverse Problems. Intelligent Computing in Optimal Design, Springe.","DOI":"10.1007\/978-3-030-34161-9"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.compstruct.2017.03.068","article-title":"The influence of laminate stacking sequence on ballistic limit using a combined Experimental\/FEM\/Artificial Neural Networks (ANN) methodology","volume":"183","author":"Varas","year":"2018","journal-title":"Compos. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mucha, W. (2019). Application of Artificial Neural Networks in Hybrid Simulation. Appl. Sci., 9.","DOI":"10.3390\/app9214495"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1177\/1045389X15577649","article-title":"Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm","volume":"26","author":"Yu","year":"2015","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1016\/j.compstruct.2009.07.019","article-title":"Manufacture and performance evaluation of the composite hat-stiffened panel","volume":"92","author":"Kim","year":"2010","journal-title":"Compos. Struct."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.cad.2006.04.014","article-title":"Comparison of FEM and BEM for interactive object simulation","volume":"38","author":"Tang","year":"2006","journal-title":"Comput.-Aided Des."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ast.2016.07.009","article-title":"Influence of nature of core on vibro acoustic behavior of sandwich aerospace structures","volume":"56","author":"Arunkumar","year":"2016","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_24","first-page":"753","article-title":"Structural Evaluation of Aircraft Stiffened Panel","volume":"5","author":"Pravallika","year":"2016","journal-title":"Int. J. Sci. Res."},{"key":"ref_25","unstructured":"Zalewski, B., and Bednarcyk, B. (2010). ACT Payload Shroud Structural Concept Analysis and Optimization."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1016\/j.matpr.2020.01.112","article-title":"Analysis of Process Parameters for Composites Manufacturing using Vacuum Infusion Process","volume":"21","author":"Gajjar","year":"2020","journal-title":"Mater. Today Proc."},{"key":"ref_27","unstructured":"Pokelwaldt, A., Adkisson, D., Arthur, B., Beach, R., Callander, D., Gundberg, T., Hobbs, W., Lacovara, R., Meringolo, M., and Nichols, G. (2019). CCT\u2014Vacuum Infusion Process: VIP."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mucha, W., Ku\u015b, W., Viana, J.C., and Nunes, J.P. (2020). Experimental Validation of Numerical Model of Composite Panel for Aerospace Structural Applications, in preparation.","DOI":"10.21495\/5896-3-358"},{"key":"ref_29","unstructured":"Ochoa, O.O., and Reddy, J.N. (2013). Finite Element Analysis of Composite Laminates. Solid Mechanics and Its Applications, Springer."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Milton, G.W. (2002). The Theory of Composites. Cambridge Monographs on Applied and Computational Mathematics, Cambridge University Press.","DOI":"10.1017\/CBO9780511613357"},{"key":"ref_31","unstructured":"(2020, March 25). Structural Analysis Software, FEA Analysis, ANSYS Structural. Available online: https:\/\/www.ansys.com\/products\/structures."},{"key":"ref_32","unstructured":"(2020, March 25). Introduction to ANSYS Composite PrepPost (ACP). Available online: https:\/\/www.ansys.com\/services\/training-center\/structures\/introduction-to-ansys-composite-preppost."},{"key":"ref_33","unstructured":"(2020, March 25). Precision Strain Gages and Sensors Databook. Available online: http:\/\/www.vishaypg.com\/docs\/50003\/precsg.pdf."},{"key":"ref_34","unstructured":"(2020, March 25). MGCplus Data Acquisition System. Available online: https:\/\/www.hbm.com\/en\/2261\/mgcplus-data-acquisition-system\/."},{"key":"ref_35","unstructured":"Zienkiewicz, O.C., and Taylor, R.L. (2000). The Finite Element Method Volume 1: The Basis, Butterworth-Heinemann. [5th ed.]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1177\/002199837100500106","article-title":"A General Theory of Strength for Anisotropic Materials","volume":"5","author":"Tsai","year":"1971","journal-title":"J. Compos. Mater."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3436","DOI":"10.1109\/TII.2017.2777460","article-title":"Levenberg-Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of A Safety Critical Cyber-Physical System","volume":"14","author":"Lv","year":"2017","journal-title":"IEEE Trans. Indust. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Menhaj Training feedforward networks with the Marquardt algorithm","volume":"5","author":"Hagan","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nguyen, D., and Widrow, B. (1990, January 17\u201321). Widrow Improving the Learning Speed of 2-Layer Neural Networks by Choosing Initial Values of the Adaptive Weights. Proceedings of the 1990 IJCNN International Joint Conference on Neural Networks, San Diego, CA, USA.","DOI":"10.1109\/IJCNN.1990.137819"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/j.engstruct.2005.11.002","article-title":"On the complexity of artificial neural networks for smart structures monitoring","volume":"28","author":"Yuen","year":"2006","journal-title":"Eng. Struct."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1111\/mice.12492","article-title":"An efficient algorithm for architecture design of Bayesian neural network in structural model updating","volume":"35","author":"Yin","year":"2020","journal-title":"Comput.-Aided Civil Infrastruct. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:24:07Z","timestamp":1760361847000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,29]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20092534"],"URL":"https:\/\/doi.org\/10.3390\/s20092534","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,29]]}}}