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Balancing the Mechanical Performance and Environmental Sustainability of Fiber-Reinforced Concrete
(2025)
Fiber-reinforced concrete (FRC) can have improved durability and tensile properties, potentially enabling the more efficient use of concrete and lowering greenhouse gas (GHG) emissions. Yet, systematic quantifications of the environmental impacts of FRC, particularly when paired with changes to mechanical properties and the implications for material longevity, are limited. Herein, an assessment following the life-cycle assessment methodology for four common FRCs was performed, namely, those reinforced with polyvinyl alcohol (PVA), steel (ST), polypropylene (PP), and polyethylene terephthalate (PET). The analysis was bound to a cradle-to-gate scope, and solely virgin fiber material production was considered for the environmental impacts. Coupled changes in compressive and tensile strength, environmental impacts, and the role of material longevity and cost relative to unreinforced concrete were examined. Findings from this work show that, similar to unreinforced concrete, cement remains a key source of GHG emissions in FRC production. However, in FRCs fibers can drive additional emissions by up to 55%. Notably, PVA and ST led to the highest impacts and costs, which were minimal for inclusions of PP and PET. Yet ST contributed to the greatest benefits in flexural and compressive strengths. When the effects of longevity were integrated, FRC with PP reinforcement could offer desired emissions reductions with minimal increase in use period and cost, but the other fiber reinforcements considered may need to offer longer service life extension to reduce emissions compared with conventional concrete. These results indicate that FRC can enhance mechanical performance, but fiber type selections should be informed by the design life to achieve actual GHG emissions reductions.
To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
