4D beyond construction: spatio-temporal and life-cyclic modeling and visualization of infrastructure data

摘要

While four-dimensional (4D) technology has been extensively studied for use in the construction phase, there exists a great potential for its successful application in the maintenance, operation, and facility management phases. This paper proposes a novel 4D-based method for life cyclic integration, modeling, and visualization of infrastructure data. Such temporal integration of infrastructure data is particularly crucial due to the ever-increasing demand for maintaining the infrastructure assets in the United States, with approximately 188 million trips occurring across the nation’s structurally deficient bridges. The proposed approach takes the application of 4D technology beyond the construction phase and demonstrates its effectiveness for life cyclic modeling and visualization of infrastructures’ data including condition assessments. In this approach, various data categories such as inspection reports, maintenance schedules and costs, degradation models, operation schedules, and elements’ semantics are integrated with 3D models and using 4D technology, bringing the notion of time and space to infrastructure data; this enables the spatio-temporal exploration and modeling of data at an element level; this is not currently achievable since the existing solutions connect and display just one set of temporal data for one level of detail of a structure. The introduced technique also visualizes the data using a color- coding scheme and provides customized query and information retrieval support. In addition, degradation models can be integrated with the 4D-based system to simulate the condition ratings over time. The technique has been implemented and demonstrated for bridge infrastructure; it has been evaluated in two case study bridges in the state of California, United States, and has shown promise for use in real-life applications. The technique was observed to become more effective with increase in the complexity of maintenance tasks and infrastructure models; it increased the accuracy of maintenance tasks by 20-40% and reduced their duration by 30-50%.