Several previous investigations on failure of a certain type lattice girders railway bridge (on so called BJD line) have not convincingly explained reasons nor have they described potential hazards. This paper attempts to provide an answer, employing static, dynamic, and fatigue analysis of the structure, focusing on previously not analyzed vibrations of elements constituting a lattice node. Detailed models of two types of such nodes - damaged and non- damaged were compared, inside carefully defined limits of applicability.

The railway viaduct WK-3 built using crosswise launching technology under the task „Trasa Górna” in Łódź. Testing program was prepared in two stages due to the railway line closure for 68hours. Due to it, rigidity of the load carrying structures could have been controlled at selected stages of the construction. Load tests made us evaluate theoretical computational model and confirm design capacity

In this paper method of dynamic analysis of structure subjected to passage of high speed trains is proposed. Application of the European Standards and results of calculations are shown. As an example single track arch railway bridge designed for a normal railway line was chosen. Authors shows that presented structure may not comply with all the conditions specified in the design code

During the load test of the road arch viaduct, it was found that it is very sensitive to dynamic effects. The occurrence of beats at the frequency of 2.50Hz was registered. In order to clarify the causes of this phenomenon, a number of numerical analyses of the structure were conducted. It was found that in the arch structure, which has infrequently distributed inclined hangers, it may be possible for the frequencies in similar forms of flexural vibrations to appear. During the operational extortion, it leads to their disadvantageous consonance. There are several technical possibilities to improve the dynamic parameters of the analysed type of the arch viaduct (assembly of mass vibration dampers, the change of bearings and change of the layout of hangers). On the basis of the conducted theoretical analyses, the installation of the monitoring system was recommended

Machine learning (ML)-based techniques have received significant attention in various fields of industry and science. In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. To evaluate the prediction capabilities of ML, this study examines the performance of several ML algorithms in estimating the total weight and location of vehicles on a bridge using strain sensing. A novel framework based on a combined model and data-driven approach is described, consisting of the establishment of the finite element (FE) model, its updating according to load testing results, and data augmentation to facilitate the training of selected physics-informed regression models. The article discusses the design of the Fiber Bragg Grating (FBG) sensor-based Bridge Weigh-in-Motion (BWIM) system, specifically focusing on several supervised regression models of different architectures. The current work proposes the use of the updated FE model to generate training data and evaluate the accuracy of regression models with the possible exclusion of selected input features enabled by the structural specificity of a bridge. The data were sourced from the SHM system installed on a network arch bridge in Wolin, Poland. It confirmed the possibility of establishing the BWIM system based on strain measurements, characterized by a reduced number of sensors and a satisfactory level of accuracy in the estimation of loads, achieved by exploiting the network arch bridge structural specificity.