Bridge structural health monitoring with the aim of continuously assessing structural safety and reliability represents a topic of major importance for worldwide infrastructure managers. In the last two decades, due to their potential economic and operational advantages, drive-by approaches experienced growing consideration from researcher and engineers. This work addresses two technical topics regarding indirect frequency estimation methods: bridge and vehicle dynamics overlapping, and bridge expansion joints impact. The experimental campaign was conducted on a mixed multi-span bridge located in Lombardy using a Ford Galaxy instrumented with a mesh of wireless accelerometers. The onboard time series were acquired for a number of 10 passages over the bridge,performed at a travelling speed of 30 km/h, with no limitations imposed to traffic. Exploiting an ad-hoc sensors positioning, pitch vehicle motion was compensated, allowing to estimate the first two bridge bending frequencies from PSD functions; moreover, the herein adopted approach proved to be insensitive to joints disturbance. Conclusively, a sensitivity study has been conducted to trace the relationship between estimation accuracy and number of trips considered in the analysis. Promising results were found, pointing out a clear positive correlation especially for the first bending frequency.

Steel truss bridge is one of the most widely used bridge types in Indonesia. Out of all Indonesia's national roads, the number of steel truss bridges reaches 12% of the total 17,160 bridges. The application of steel truss bridges is relatively high considering this type of bridge provides advantages in the standardization of design and fabrication of structural elements for typical bridge spans, as well as ease of mobilization. Directorate of Road and Bridge Engineering, Ministry of Works and Housing, has issued a standard design for steel truss bridges commonly used in Indonesia, which is designed against the design load in SNI 1725-2016 Bridge Loading Standards. Along with the development of actual traffic load measurement technology using Bridge Weigh-in-Motion (B-WIM), traffic loading data can be utilized to evaluate the reliability of standard bridges, such as standard steel truss bridges which are commonly used in Indonesia. The result of the B-WIM measurement on the Central Java Pantura National Road, Batang - Kendal undertaken in 2018, which supports the heaviest load and traffic conditions on the national road, is used in this study. In this study, simulation of a sequences of traffic was carried out based on B-WIM data as a moving load on the Australian type Steel Truss Bridge (i.e., Rangka Baja Australia -RBA) structure model with 60 m class A span. The reliability evaluation was then carried out by calculating the reliability index or the probability of structural failure. Based on the analysis conducted in this study, it was found that the reliability index of the 60 m class Aspan for RBA bridge is 3.04 or the probability of structural failure is 1.18 × 10^-3, which describes the level of reliability of the RBA bridge structure due to the loads from B-WIM measurement in Indonesia. For this RBA Bridge 60 m span class A, it was found that the calibrated nominal live load that met the target reliability is increased by 13% than stated in the code, so the uniform distributed load will be 7.60 kN/m² and the axle line equivalent load will be 55.15 kN/m.

The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.

Bridges are critical to the civil engineering infrastructure network as they facilitate movement of people, the transportation of goods and services. Given the aging of bridge infrastructure, federal officials mandate visual inspections biennially to identify necessary repair actions which are time, cost, and labor-intensive. Additionally, the expansion joints of bridges are rarely monitored due to cost. However, expansion joints are critical as they absorb movement from thermal effects, loadings strains, impact, abutment settlement, and vehicle motion movement. Thus, the need to monitor bridge expansion joints efficiently, at a low cost, and wirelessly is desired. This paper addresses bridge joint monitoring needs to develop a cost-effective, real-time wireless system that can be validated in a full-scale bridge structure. To this end, a wireless expansion joint monitoring was developed using commercial-off-the-shelf (COTS) sensors. An in-service bridge was selected as a testbed to validate the performance of the developed system compared with traditional displacement sensor, LVDT, temperature and humidity sensors. The short-term monitoring campaign with the wireless sensor system with the internet protocol version 6 over the time slotted channel hopping mode of IEEE 802.15.4e (6TiSCH) network showed reliable results, providing high potential of the developed system for effective joint monitoring at a low cost.

A USW based diagnostic procedure is presented for estimating the depth of surface-breaking cracks. The diagnosis is demonstrated on seven lab-scale SFRC beam specimens, which are subjected to the CMOD controlled three-point bending test to create real bending cracks. Then, the recorded multiple ultrasonic signals are examined with the signal processing techniques, including wavelet transform and two-dimensional Fourier transform, to investigate the relationships between the crack depth and two diagnostic indices, namely the attenuation coefficient and dispersion index (DI). Finally, the reliabilities of these indices for depth estimation are verified with the visually measured crack depths as well as the crack features obtained with a digital image processing algorithm. It is found that the DI outperforms the attenuation coefficient in depth estimation, where this index displays good agreement with the visual inspection for 86% of the inspected specimens.