João Nuno Fernandes

Abstract
An autonomous vehicle relying on LiDAR data should be able to assess its limitations in real time without depending on external information or additional sensors. The point cloud generated by the sensor is subjected to significant degradation under adverse weather conditions (rain, fog, and snow), which limits the vehicle's visibility and performance. With this in mind, we show that point cloud data contains sufficient information to estimate the weather accurately and present MobileWeatherNet, a LiDAR-only convolutional neural network that uses the bird's-eye view 2D projection to extract point clouds' weather condition and improves state-of-the-art performance by 15% in terms of the balanced accuracy while reducing inference time by 63%. Moreover, this paper demonstrates that among common architectures, the use of the bird's eye view significantly enhances their performance without an increase in complexity. To the extent of our knowledge, this is the first approach that uses deep learning for weather estimation using point cloud data in the form of a bird's-eye-view projection.

Abstract
Physical models and probabilistic applications often guide the study and characterization of natural phenomena in engineering. Such is the case of the study of air change rates (ACHs) in buildings for their complex mechanisms and high variability. It is not uncommon for the referred applications to be costly and impractical in both time and computation, resulting in the use of simplified methodologies and setups. The incorporation of airtightness limits to quantify adequate ACHs in national transpositions of the Energy Performance Building Directive (EPBD) exemplifies the issue. This research presents a roadmap for developing an alternative instrument, a compliance tool built with a Machine Learning (ML) framework, that overcomes some simplification issues regarding policy implementation while fulfilling practitioners' needs and general societal use. It relies on dwellings' terrain, geometric and airtightness characteristics, and meteorological data. Results from previous work on a region with a mild heating season in southern Europe apply in training and testing the proposed tool. The tool outputs numerical information on the air change rates performance of the building envelope, and a label, accordingly. On the test set, the best regressor showed mean absolute errors (MAE) below 1.02% for all the response variables, while the best classifier presented an average accuracy of 97.32%. These results are promising for the generalization of this methodology, with potential for application at regional, national, and European Union levels. The developed tool could be a complementary asset to energy certification programmes of either public or private initiatives. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract
Energy efficiency and indoor air quality are frequently-two conflicting objectives when establishing the air change rate (ACH) of a dwelling. In Europe, the northern countries have a clear focus on energy conservation, leading to an obvious awareness of the importance of airtightness, which translates into a high level of regulation and implementation. Meanwhile, the southern counterparts experience a more com-plex challenge by having predominantly passive ventilation strategies and milder climates, which often results in a more permissive approach. This work proposes an innovative labelling methodology to classify the performance of naturally ventilated dwellings. A representative sample of a southern European national built stock is used in a stochastic process to create a pool of 43,200 unique dwellings. The simulation period refers to a month of the typical heating season in the southern European mild conditions. The results test the labelling methodology. With feature selection, ACH limits, and a labelling strategy, dwellings classify according to their ability to provide adequate ACHs. The terrain was the best splitter of the dataset from the applied categorical variables. Regarding continuous variables, the airtightness was the one explaining most of the variability of the outputted ACHs, followed by the floor area. From the best performing dwellings labelled as compliant (Com), the average airtightness level was 5.3 h(-1), with 4.9 h(-1) and 5.8 h(-1) in rural and urban locations.

Abstract
In the context of bridge management, three main types of maintenance actions can be considered. Maintenance actions can be taken preventively before the predefined limit condition is reached, or as a corrective measure in case those limits have been reached. The third possibility corresponds to the so-called doing nothing scenario, in which no action is taken on the bridge. To be able to implement preventive maintenance, it is necessary to know the current condition of the bridge, as well as to be able to predict its performance. On the other hand, it is also important to be able to identify potentially threatening events that might occur in the analysis life period. This paper describes an integrated methodology to help bridge managers in defining an efficient maintenance program, considering the specific case of a railway bridge. The novelty of the methodology is focused on updating an existing methodology proposed by COST TU1406, by extending it to railway bridges and also by including the resilience analysis in case of a sudden event occurrence. The analysis considers a multi-hazard future scenario, in which a flood event occurs while corrosion phenomena were already in place. The results show the feasibility of the proposed methodology as a support for the establishment of an efficient maintenance schedule to prevent bridge severe degradation, as well as to establish recovery plans in case of a sudden event.

Abstract
This paper presents a computational framework for the optimization of maintenance activities for infrastructure assets, with particular emphasis being placed on road network assets. This framework incorporates degradation and maintenance models for infrastructure assets along with multi-objective optimization for searching optimal maintenance schedules. Given a schedule of maintenance actions, the future performance is estimated by means of a Monte Carlo simulation that enables to account for inherent uncertainties. The design variables of optimization are the types of maintenance actions and their timing over the planning horizon. The objectives are to minimize both the asset degradation and maintenance cost. This includes satisfaction of constraints representing performance demands. The proposed framework is general and can be applied to different types of infrastructure assets. The numerical results, obtained for a road bridge managed by a highway operating agency, demonstrate the validity and usefulness of the proposed framework.