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Collecting information on heavy trucks and monitoring the bridges which they regularly cross is important for many facets of infrastructure management. In this paper, a two-step algorithm is developed using bridge and truck data, by deploying sequentially unsupervised and supervised machine learning techniques. Longitudinal clustering of bridge data, concerning strain waveforms, is adopted to perform the first step of the algorithm, while image visual inspection and classification tree methods are applied to truck data concurrently in the second step. Both bridge and truck traffic must be monitored for a limited, yet significant, amount of time to calibrate the algorithm, which is then used to build a classification framework. The framework provides the same benefits of two data collection systems while only one needs to be operative. Depending on which monitoring system remains available, the framework enables the use of bridge data to identify the truck’s profile which generated it, or to estimate bridge response given the truck’s information. As a result, the present study aims to provide decision-makers with an effective way to monitor the whole bridge-traffic system, bridge managers to plan effective maintenance, and policymakers to develop ad hoc regulations.

Monitoring bridge performance is crucial to ensure safety and allocate resources in a cost-effective manner. This paper aims to reduce the gap between researchers and practitioners by showing how predictive analytics can be employed in the process of distilling operational information out of bridge monitoring data. Furthermore, it has the goal to aid infrastructure owners and managers in evaluating bridge performance over time and making data-driven decisions to prolong the life of the structure. To achieve this goal, the paper presents a comparative study of three predictive analysis models to estimate bridge response to heavy trucks: multilinear regression, artificial neural network, and regression tree. Following this comparison, an alternative strategy, based on the analysis of influential observations, is proposed. This approach brings together predictive power with other important capabilities such as explanatory capabilities and interpretability. The test bed structure is a short-span highway bridge which was monitored for 3 years using weigh-in-motion (traffic data) and structural health monitoring (bridge data) systems.

A situation on a large cantilever through-truss bridge, the John F. Kennedy Memorial Bridge, which carries I-65 over the Ohio River at Louisville, Kentucky, provides significant opportunities for applying innovative health monitoring techniques to transportation infrastructure. One of four anchor bolts restraining an uplift bearing at one end of the bridge fractured as a result of corrosion fatigue; the fracture led to concerns about the continuing safety and serviceability of the compromised bearing assembly. Short- and long-term instrument-based monitoring systems were deployed on the bearing assembly to (a) characterize the behavior of the damaged bearing and its intact counterparts, (b) quantify the load transfer due to the retrofit, and (c) monitor the performance of the retrofit continuously for the long term. The monitoring strategy has been successful on all counts; in particular, during Part 3 the system detected a significant shifting of loads that was later confirmed to correspond to fracture of a threaded rod that had been used to replace the failed anchor bolt; this result showed the utility of continuous remote monitoring in detecting failures between inspections. This paper describes the instrumentation plan and strategies for data acquisition, communication, and autonomous operation used on the bridge. The data and implications for management decisions are also discussed.