Effective transportation network management systems should consider safety and sustainability objectives. Existing research on large-scale transportation network management often employs the assumption that bridges can be considered individually under these objectives. However, this simplification misses accurate system-level representations, induced by multiple components, network topology, and global maintenance actions. To address these limitations, this paper presents a deep reinforcement learning (DRL) framework that draws inspiration from biological learning behaviors to determine optimal life-cycle management policies. It incorporates synergetic branches and hierarchical rewards, factorizing the action space and, thereby, diminishing system complexity from exponential to linear with respect to the number of bridges. Extensive experiments based on a realistic case study demonstrate that the proposed method outperforms expert maintenance strategies and state-of-the-art decision-making methods. Overall, the proposed DRL framework can assist engineers by offering adaptive solutions to maintenance planning. It also provides solutions that address large action spaces within complex systems.

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The accumulation of phosphorus in activated sludge in wastewater treatment plants (WWTPs) provides potential for phosphorus recovery from sewage. This study delves into the potential for releasing phosphorus from waste activated sludge through two distinct treatment methods—thermal hydrolysis and pH adjustment. The investigation was conducted with activated sludge sourced from four WWTPs, each employing distinct phosphorus removal strategies. The findings underscore the notably superior efficacy of pH adjustment in solubilizing sludge phosphorus compared to the prevailing practice of thermal hydrolysis, widely adopted to enhance sludge digestion. The reversibility of phosphorus release within pH fluctuations spanning 2 to 12 implies that the release of sludge phosphorus can be attributed to the dissolution of phosphate precipitates. Alkaline sludge treatment induced the concurrent liberation of COD, nitrogen, and phosphorus through alkaline hydrolysis of sludge biomass and the dissolution of iron or aluminium phosphates, offering potential gains in resource recovery and energy efficiency.

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Spectral power distribution (SPD) is the radiation power intensity at different wavelengths, containing the most basic photometric and colorimetric performance of the illuminant, which is able to predict the lifetime of LEDs. This paper proposes an SPD model assisted by machine learning algorithms to detect the early failure of white LEDs. The SPD features of 3W high-power white LEDs were firstly extracted by the statistical models of Gaussian, Lorentz, and Asym2sig functions. An unsupervised learning method, principal component analysis (PCA), was then used to reduce the extracted features parameters’ dimensions. Next a K-nearest neighbor (KNN)-based method was used to detect LEDs’ anomalies by dividing the main cluster into groups, and estimating the distance from the center of mass of each cluster to the test point. The results showed the following: (1) for selected white LEDs, the Asym2sig function has a better fitting result than Gaussian and Lorentz functions; (2) machine learning methods can significantly assist in LED anomaly detection and can decrease the amount of anomaly detection time to 789.6 h, compared to the 1311 h when lumen maintenance degradation reaches 70% as required by IES TM21.@en