Cost-Effectiveness of Alternative Retrofit Options to Support Decision Making

How to evaluate the cost-effectiveness of repair/retrofit intervention vs. demolition/replacement and what level of shaking intensity can the chosen repairing/retrofit technique sustain are open questions affecting either the pre-earthquake prevention, the post-earthquake emergency and the reconstruction phases. The (mis)conception that the cost of retrofit interventions would increase linearly with the achieved seismic performance (%NBS) often discourages stakeholders to consider repair/retrofit options in a post-earthquake damage situation. Similarly, in a pre-earthquake phase, the minimum (by-law) level of %NBS might be targeted, leading in some cases to no-action. Furthermore, the performance measure enforcing owners to take action, the %NBS, is generally evaluated deterministically. Not directly reflecting epistemic and aleatory uncertainties, the assessment can result in misleading confidence on the expected performance. The present study aims at contributing to the delicate decision-making process of repair/retrofit vs. demolition/replacement, by developing a framework to assist stakeholders with the evaluation of the effects in terms of long-term losses and benefits of an increment in their initial investment (targeted retrofit level) and highlighting the uncertainties hidden behind a deterministic approach. For a pre-1970 case study building, different retrofit solutions are considered, targeting different levels of %NBS, and the actual probability of reaching Collapse when considering a suite of ground-motions is evaluated, providing a correlation between %NBS and Risk. Both a simplified and a probabilistic loss modelling are then undertaken to study the relationship between %NBS and expected direct and indirect losses.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.