In this paper, the performance of deteriorating bridges is analyzed considering the combination of essential and preventive maintenance actions. Multiobjective optimization under uncertainty is used to find the best combinations of condition, safety, and cost; satisfying all performance constraints of deteriorating bridges during a specified time horizon under multiple maintenance types. The thresholds at which essential maintenance actions are applied and the times of application of preventive maintenance actions are considered as design variables. The evolution in time of nondeterministic performance is modeled as probabilistic condition, safety, and cost profiles, and genetic algorithms are combined with Latin hypercube sampling to optimize the maintenance strategies in a full probabilistic context. Owing to the scarce information on the structural performance effects, due to the interaction among different types of maintenance actions, special attention is paid to the assumptions with respect to the c...

This paper presents the application of three Machine Learning techniques to fuel consumption modelling of articulated trucks for a large dataset. In particular, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models have been developed for the purpose and their performance compared. Fleet managers use telematic data to monitor the performance of their fleets and take decisions regarding maintenance of the vehicles and training of their drivers. The data, which include fuel consumption, are collected by standard sensors (SAE J1939) for modern vehicles. Data regarding the characteristics of the road come from the Highways Agency Pavement Management System (HAPMS) of Highways England, the manager of the strategic road network in the UK. Together, these data can be used to develop a new fuel consumption model, which may help fleet managers in reviewing the existing vehicle routing decisions, based on road geometry. The model would also be useful for road managers to better understand the fuel consumption of road vehicles and the influence of road geometry. Ten-fold cross-validation has been performed to train the SVM, RF, and ANN models. Results of the study shows the feasibility of using telematic data together with the information in HAPMS for the purpose of modelling fuel consumption. The study also shows that although all the three methods make it possible to develop models with good precision, the RF slightly outperforms SVM and ANN giving higher R², and lower error.