Citation: Safaei, M.; Hejazian, M.; Pedrammehr, S.; Pakzad, S.; Ettefagh, M.M.; Fotouhi, M. Damage Detection of Gantry Crane with a Moving Mass Using Artificial Neural Network. Buildings 2024, 14, 458. https:// doi.org/10.3390/buildings14020458 Academic Editors: Ping Xiang, Huaping Wang and Pengfei Cao Received: 29 December 2023 Revised: 1 February 2024 Accepted: 4 February 2024 Published: 7 February 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). buildings Article Damage Detection of Gantry Crane with a Moving Mass Using Artificial Neural Network Mohammad Safaei 1 , Mahsa Hejazian 1 , Siamak Pedrammehr 2 , Sajjad Pakzad 2 , Mir Mohammad Ettefagh 1 and Mohammad Fotouhi 3, * 1 Faculty of Mechanical Engineering, University of Tabriz, Tabriz 5166616471, Iran; safaei833@gmail.com (M.S.); mahsahejazian@gmail.com (M.H.); ettefagh@tabrizu.ac.ir (M.M.E.) 2 Faculty of Design, Tabriz Islamic Art University, Tabriz 5164736931, Iran; s.pedrammehr@tabriziau.ac.ir (S.P.); s.pakzad@tabriziau.ac.ir (S.P.) 3 Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands * Correspondence: m.fotouhi-1@tudelft.nl Abstract: Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced using random changes in the stiffness of different parts of the structure. Contrary to the assumption of inherent reliability, undetected defects in crucial structural elements can lead to catastrophic failures. Then, the vibration equations of healthy and damaged models are analyzed to find the displacement, velocity, and acceleration of the different crane parts. The learning vector quantization neural network is used to train and detect the defects. The output is the location of the damage and the damage severity. Noisy data are then used to evaluate the network performance robustness. This research also addresses the limitations of traditional inspection methods, providing early detection and classification of defects in gantry cranes. The study’s relevance lies in the need for a comprehensive and efficient damage detection method, especially for components not easily accessible during routine inspections. Keywords: gantry crane; structural damage detection; finite element model; artificial neural network; learning vector quantization (LVQ) 1. Introduction Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are common, certain defects, particularly in less accessible components, remain challenging to detect early. This research introduces a novel approach utilizing a learning vector quantization (LVQ) neural network for precise defect detection in gantry cranes. Contrary to the assumption of inherent reliability, unde- tected defects in crucial structural elements can lead to catastrophic failures. The current study is motivated by the need for an advanced defect detection method to enhance crane safety and reliability. With the advances in the transportation industry, the mass, the size, and the crane structure strength have improved, but the crane structure stiffness has not improved as much. This means that the dynamic response of the crane is affected by the moving payload and changes as the trolley moves or with a change in payload and its speed, and it could cause bending, cracking, or failure at different regions of the crane structure. Another problem with the gantry crane is the inherent vibrations of a suspending payload. Using the moving load in the dynamics of cranes in engineering research has received special attention in recent years; but unfortunately, not many studies are available about the im- pact of the moving load on the modeling, dynamic simulation, and damage assessment of gantry cranes [1]. Gantry crane dynamics consist of structural and suspended-load Buildings 2024, 14, 458. https://doi.org/10.3390/buildings14020458 https://www.mdpi.com/journal/buildings