Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

Abstract

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.

Research on Life-Cycle Project Cost Management Based on Random Matrix Weight Algorithm

“…e adaptive parameter setting formula of the corresponding stage target expansion state is shown in formula (8), where the corresponding matrix A is set as a positive definite matrix parameter, and the corresponding adaptive noise parameter setting formula is shown in formula (9). Here, it is assumed that the noise parameter model weights of the corresponding adjacent stages are approximately equal.…”

“…rough reasonable full life-cycle project cost management, the costs related to project construction, operation, and management can be greatly reduced, the green ecological benefits of construction projects can be further improved, social development and human needs can be met, social benefits can be significantly improved, and the importance of project cost management for engineering projects can be deeply reflected [8,9]. Based on this, the current conventional construction industry full life-cycle project cost management schemes are diverse, most of which focus on the single purpose of how to improve the construction cost, while ignoring the linkage of other elements in the project cost management [10].…”