首页|Findings in the Area of Machine Learning Reported from University of Colorado De nver (Machine Learning-based Bridge Maintenance Optimization Model for Maximizin g Performance Within Available Annual Budgets)
Findings in the Area of Machine Learning Reported from University of Colorado De nver (Machine Learning-based Bridge Maintenance Optimization Model for Maximizin g Performance Within Available Annual Budgets)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting out of Denver, Colorado, by NewsRx edito rs, research stated, "Effective maintenance planning for bridges is crucial for maintaining their performance, safety, and minimizing maintenance costs. Timely implementation of interventions can improve the performance of bridges and avoid the need for costly interventions." Financial support for this research came from Mountain Plan Consortium (MPC). Our news journalists obtained a quote from the research from the University of C olorado Denver, "However, bridge maintenance is often delayed because of inadequ ate planning and budget allocation, as well as resource constraints such as fund ing. With the availability of historical condition data of bridges in databases such as the National Bridge Inventory (NBI) and National Bridge Elements (NBE), there is an opportunity to use data-driven methods to predict deterioration of b ridge elements and optimize their maintenance interventions to maximize the perf ormance of bridges. This paper presents the development of a novel system that u ses machine learning (ML) techniques, to predict the condition of concrete bridg e elements, and binary linear programming optimization method, to identify the o ptimal selection of maintenance interventions and their timing, to maximize the performance of bridges while complying with available annual budgets. Four ML me thods are explored: decision tree, random forest, gradient boosting, and support vector machines. The results of the ML evaluation show that, while the values o f the predictive performance metrics varied for different elements, random fores t method had the best performance for all elements. A case study of a concrete b ridge is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The case study results show that the developed model identifie s optimal maintenance interventions for various annual budgets over a 50-year st udy period. The primary contributions of this research to the body of knowledge are as follows: (1) the development of a novel system that integrates machine le arning techniques and linear programming for predicting bridge element condition s and optimizing maintenance interventions; (2) modeling and predicting the dete rioration of bridge elements based on health index metric; and (3) generating lo ng-term maintenance plans for each of the bridge elements to maximize the perfor mance of bridges within available annual budgets."
DenverColoradoUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Colorado Denver
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