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Due to the influence of diverse factors, travel time is highly uncertain. Travelers are eager to find the most reliable path in multimodal networks to reduce the penalty caused by late arrival. However, the research considering the traveler preferences in multimodal transportation networks to solve the reliable path problem with given budgets is limited. Thus, we propose two multimodal reliable path models to find personalized and reliable paths. First, we build a multimodal network based on smart card data to incorporate the multimodal transfers between public and private transportation and solve corresponding parking issues effectively. Next, we build a multimodal time-reliable path model to find time-reliable paths. Further, considering traveler preferences, we design a multimodal utility-reliable path model to find personalized and reliable paths. A novel two-factor reliability bound convergence algorithm is developed to solve the proposed models and proved for its theoretical feasibility. Finally, a real-world case study is used to verify the effectiveness and efficiency of the proposed models and algorithm.

Water has covered a wide part of the earth’s surface. Oceans and other water bodies contain significant natural and environmental resources as well as aquatic life. Due to humans’ hazardous and unsuitable underwater (UW) settings, these are generally undiscovered and unknown. As a result of its widespread utility in fields as diverse as oceanography, ecology, seismology, and oceanography, underwater wireless sensor networks (UWSNs) have emerged as a cutting-edge area of study. Despite their usefulness, the performance of the network is hampered by factors including excessive propagation delay, a changing network architecture, a lack of bandwidth, and a battery life that is too short on sensor nodes. Developing effective routing protocols is the best way to overcome these challenges. An effective routing protocol can relay data from the network’s root node to its final destination. Therefore, the state of the art in underwater wireless acoustic sensor network (UWASN) routing protocols is assessed with an eye toward their potential for development. In real-world applications, sensor node positions are frequently used to locate relevant information. As a result, it is crucial to conduct research on routing protocols. Reinforcement learning (RL) algorithms have the ability to enhance routing under a variety of conditions because they are experience-based learning algorithms. Underwater routing methods for UWSN are reviewed in detail, including those that rely on machine learning (ML), energy, clustering and evolutionary approaches. Tables are incorporated for the suggested protocols by including the benefits, drawbacks, and performance assessments, which make the information easier to digest. Also, several applications of UWSN are discussed with security considerations. In addition to this, the analysis of node deployment and residual energy is discussed in this review. Furthermore, the domain review emphasizes UW routing protocol research difficulties and future directions, which can help researchers create more efficient routing protocols based on ML in the future.

This chapter poses the damage detection for civil, mechanical, and aerospace structures in the context of a pattern recognition paradigm for structural health monitoring (SHM), where machine learning algorithms are essential to learn the structural behavior from experience, following the same principle of the human brain. These algorithms are especially relevant in cases where the damage-sensitive features extracted from the structural responses are affected by changes caused by operational and environmental variability and changes caused by damage. State-of-the-art machine learning algorithms are presented based on Mahalanobis squared distance (MSD), Gaussian mixture models (GMMs), principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN); the bioinspired algorithms are highlighted as promising algorithms to overcome some of the limitations of the more traditional ones. All the algorithms present different working principles and seek to generalize the normal structural condition in order to detect deviations, from the baseline condition, associated with damage. The applicability of the chosen algorithms for damage detection will be tested on standard data sets from the Z-24 Bridge in Switzerland. These data sets are unique as they combine 1-year monitoring from the baseline condition, when influenced by extreme operational and environmental variability, with realistic damage scenarios.