Bridges are particularly vulnerable elements of transport infrastructures. In many cases, bridge structures may be subject to higher volumes of traffic and higher loads as well as more severe environmental conditions than it was designed. Sound procedures to ensure monitoring, quality control, and preventive maintenance systems are therefore vital. The paper presents main challenges and arriving possibilities in management of bridge structures, including: relationships between environment and bridge infrastructure, improvement of diagnostic technologies, advanced modelling of bridges in computer-based management systems, development of knowledge-based expert systems with application of artificial intelligence, applications of technology of Bridge Information Modelling (BrIM) with augmented and virtual reality techniques. Presented activities are focused on monitoring the safety of bridges for lowering the risk of an unexpected collapse significantly as well as on efficient maintenance of bridges as components of transport infrastructure – by means of integrated management systems. The proposed classification of Bridge Management Systems shows the history of creating such systems and indicates the expected directions of their development, taking into account changing challenges and integrating new developing technologies, including automation of decision-making processes.

The study presents the finite element (FE) model update of the existing simple-spans steelconcrete composite bridge structure using a particle swarm optimisation (PSO) and genetic algorithm (GA) approaches. The Wireless Structural Testing System (STS-WiFi) of Bridge Diagnostic, Inc. from the USA, implemented various types of sensors including: LVDT displacement sensors, intelligent strain transducers, and accelerometers that the static and dynamic historical behaviors of the bridge structure have been recorded in the field testing. One part of all field data sets has been used to calibrate the cross-sectional stiffness properties of steel girders and material of steel beams and concrete deck in the structural members including 16 master and slave variables, and that the PSO and GA optimisation methods in the MATLAB software have been developed with the new innovative tools to interface with the analytical results of the FE model in the ANSYS APDL software automatically. The vibration analysis from the dynamic responses of the structure have been conducted to extract four natural frequencies from experimental data that have been compared with the numerical natural frequencies in the FE model of the bridge through the minimum objective function of percent error to be less than 10%. In order to identify the experimental mode shapes of the structure more accurately and reliably, the discrete-time state-space model using the subspace method (N4SID) and fast Fourier transform (FFT) in MATLAB software have been applied to determine the experimental natural frequencies in which were compared with the computed natural frequencies. The main goal of the innovative approach is to determine the representative FE model of the actual bridge in which it is applied to various truck load
configurations according to bridge design codes and standards. The improved methods in this document have been successfully applied to the Vietnamese steel-concrete composite bridge in which the load rating factors (RF) of the AASHTO design standards have been calculated to predict load limits, so the final updated FE model of the existing bridge is well rated with all RF values greater than 1.0. The presented approaches show great performance and the potential to implement them in industrial conditions.

Monitoring and structural health assessment are the primary requirements for performance evaluation of damaged bridges. This paper highlights the case-study of a damaged Reinforced Concrete (RC) bridge structure by considering the outcomes of destructive testing, Non-Destructive Testing (NDT) evaluations, static and 3D non-linear analysis methods. Finite element (FE) modelling of this structure is being done using the material properties extracted by the in-situ testing. Analysis is carried out to evaluate the bridge damage based on the data recorded after the static linear (AXIS VM software) and 3D non-linear analysis (ATENA 3D software). Extensive concrete cracking and high value of crack width are found to be the major problems, leading to lowering the performance of the bridge. As a solution, this paper proposes a proper Structural Health Monitoring (SHM) system, that will extend the life cycle of the bridge with minimal repair costs and reduced risk of failure. This system is based on the installation of three different types of sensors: Liquid Levelling sensors (LLS) for measurement of vertical displacement, Distributed Fiber Optic Sensors (DFOS) for crack monitoring, and Weigh in Motion (WIM) devices for monitoring of moving loads on bridge.

Structural health monitoring (SHM) of bridges is constantly upgraded by researchers and bridge engineers as it directly deals with bridge performance and its safety over a certain time period. This article addresses some issues in the traditional SHM systems and the reason for moving towards an automated monitoring system. In order to automate the bridge assessment and monitoring process, a mechanism for the linkage of Digital Twins (DT) and Machine Learning (ML), namely the Support Vector Machine (SVM) algorithm, is discussed in detail. The basis of this mechanism lies in the collection of data from the real bridge using sensors and is providing the basis for the establishment and calibration of the digital twin. Then, data analysis and decision-making processes are to be carried out through regression-based ML algorithms. So, in this study, both ML brain and a DT model are merged to support the decision-making of the bridge management system and predict or even prevent further damage or collapse of the bridge. In this way, the SHM system cannot only be automated but calibrated from time to time to ensure the safety of the bridge against the associated damages.