The accelerated deterioration of reinforced concrete (RC) road bridges built in the post-World War II period, combined with increasingly stringent seismic-performance requirements, calls for structured and scientifically grounded procedures for selecting retrofit strategies. This paper proposes an integrated four-phase methodology combining (i) calculation of the verified safety index (ISV) according to the VAL4 procedure; (ii) estimation of the discounted life-cycle cost (LCC) over a 100-year horizon; (iii) GIS analysis of alternative traffic routes; and (iv) a two-level simple additive weighting (SAW) multi-criteria analysis, supported by five deterministic weighting scenarios. The framework is applied to a three-span Gerber-type RC road bridge (L = 78 m), classified in the highest urgency category (attention class CdA 1) according to Italian bridge-management guidelines, with an initial seismic safety index ISV = 0.06. Three intervention alternatives are compared: (A) in-situ structural upgrading with carbon-fibre-reinforced polymers (CFRP) and concrete jacketing; (B) partial demolition of the central span with FRP upgrading of the retained spans; and (C) full demolition and reconstruction. Alternative C obtains the highest base-case SAW score and ranks first in four of the five weighting scenarios examined and in about 99% of 20,000 randomly sampled weight vectors; Alternative A prevails only when traffic impact receives a weight close to its upper bound and seismic safety a weight close to its lower bound. At the base real discount rate of 2%, the 100-year LCC values are EUR 3.98 M for C, EUR 4.45 M for A, and EUR 5.84 M for B, preserving the same ranking as the undiscounted estimates. The case study therefore shows that full replacement may be both structurally and economically preferable. The proposed framework offers a transparent and replicable decision-support procedure for public authorities managing seismically vulnerable bridge assets.
Railway tracks on bridges are subject to longitudinal movements caused by temperature-induced stresses and train traction/braking forces, which generate track–bridge interaction and additional stresses in the rail. Rail expansion joints can compensate for these effects but are prone to malfunction and incur high maintenance costs. The technical regulations defining the basis for rail dimensioning may warrant re-evaluation, as they do not fully account for the current technical progress of superstructure systems. This paper reviews the state of the art in track–bridge interaction, focusing on rail longitudinal forces and deformations under thermal and traction/braking loads. The fatigue strength of railway rails, the action and dissipation of longitudinal forces, and track buckling are addressed first, followed by the mathematical principles governing rail longitudinal forces and deformations. Rail expansion joints and the key factors influencing track–bridge interaction are then discussed, along with field measurement approaches applied in related domains and the identified research gap. Finally, this gap is used to outline the need for future field investigations of the actual longitudinal forces and deformations in rail expansion joints on bridges, providing a basis for validating existing dimensioning verification methods for the rail.
Wire breakage alters strand continuity, interwire contact, load redistribution, and modal response. This study evaluates normalized frequency changes as a laboratory fingerprint of persistent mechanical changes in unloaded seven-wire helical strands. Six 12.7 mm, 1 + 6 ASTM A586-18 strands with free lengths of 200 mm (S20) and 400 mm (S40) were tested in tension. Dynamic measurements comprised five repeated impacts on one strand per length in the intact state, after the elastoplastic transition, and during progressive wire breakage. Tensile records and optical observations documented nonsimultaneous fracture and intermediate load-bearing configurations. After unloading from the elastoplastic transition, the fundamental flexural frequency reductions were 5.6% and 6.3%, compared with a maximum repeat-impact coefficient of variation of 0.3%. A maximum reduction of 18% was reported during the wire-break sequence. A comparison using the same experimental summaries shows that direct frequency shift and the squared-frequency index yield identical detection decisions under corresponding thresholds. Sensitivity calculations quantify the effect of the threshold and modal-mass assumption, and published strand experiments provide an external comparison. The contribution is an experimentally grounded interpretation of residual frequency changes as a global state indicator. The results support a laboratory proof-of-concept for the two tested strands, with equivalent stiffness loss interpreted under explicit modal assumptions.
The risk-based importance ranking method takes into account both the failure probability and the network-level consequences of urban bridges, having a significant advantage in guiding the allocation of maintenance funds under limitation conditions. This paper conducts a study on the importance ranking of urban bridges based on periodic inspection data from 445 highway bridges in a Chinese city. Urban transportation network data is extracted to construct an urban bridge network consisting of 186 nodes and 328 edges. A multi-dimensional importance indicator considering safety, social, functional, and environmental factors is established to comprehensively characterize the consequences of bridge failure. The edge on which a bridge is located is removed to simulate bridge failure for network calculation and to obtain the importance indicator. Results show that the consequences of failure in the functional and social dimensions constitute the primary and secondary components of the total consequences of bridge failure, respectively. The former accounts for 61.15%, while the latter accounts for 23.92%. In contrast, the safety indicator, representing structural repair, accounts for only 3.61%. The influence of bridge failure at the network level is significantly greater than that at the bridge level. The value of the present work lies in integrating these dimensions and demonstrating their combined effect on network-level ranking.
In this study, a novel physics-informed hybrid deep learning (PI-HDL) algorithm is proposed for intelligent condition assessment of metro tunnel structures. The algorithm integrates state-of-the-art deep learning models, including auto-encoder and convolutional neural network (CNN) models. Physics-inspired constraints derived from structural dynamics have been embedded into the loss functions of these models. Via an effective learning, merging, and training methodology, the designed model reconstructs high-level features of 1D time-domain vibration response signals in an unsupervised manner. Then, these physics-consistent high-level features are fed into a supervised 2D CNN trained with a composite loss function that incorporates both data fidelity and physics-based residuals derived from the stiffness–frequency relationship and moving load characteristics. The developed methodology is evaluated using vibration signals collected numerically from a concrete metro tunnel structure under various moving train loads and damage scenarios. The proposed PI-HDL framework embeds the underlying physics of wave propagation and structural dynamics directly into the learning process. This enables accurate identification of predefined damage conditions and stiffness reduction quantification within a closed set of ten structural scenarios, even with limited training data and noisy measurements. Comparisons prove that the PI-HDL algorithm achieves superior closed-set classification performance compared to purely data-driven HDL approaches.
