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Smart sensors are technologies designed to facilitate the monitoring operations. For instance, power consumption can be minimized through on-board processing and smart interrogation algorithms, and state detection enhanced through collaboration between sensor nodes. Applied to structural health monitoring, smart sensors are key enablers of sparse and dense sensor networks capable of monitoring full-scale structures and components. They are also critical in empowering operators with decision making capabilities. The objective of this Special Issue is to generate discussions on the latest advances in research on smart sensing technologies for structural health monitoring applications, with a focus on decision-enabling systems. This Special Issue covers a wide range of related topics such as innovative sensors and sensing technologies for crack, displacement, and sudden event monitoring, sensor optimization, and novel sensor data processing algorithms for damage and defect detection, operational modal analysis, and system identification of a wide variety of structures (bridges, transmission line towers, high-speed trains, masonry light houses, etc.).

NSGA-II --- wind force --- wavelet packet decomposition --- structural health monitoring --- amplitude spectrum --- environmental noise --- patch antenna --- damage identification --- event-triggered sensing --- Particle Swarm Optimization (PSO) algorithm --- high-speed train --- low-velocity impacts --- concrete structures --- sensors distribution optimization --- acceleration --- digital sampling moiré --- crack --- displacement sensor --- length effect --- FBG sensor array --- SHM --- space window --- Bayesian blind source separation --- feature selection --- stress detection --- wheel minor defect --- strain --- uniaxial stress measurement --- turbine --- impact identification --- helical antenna --- energy analysis of wavelet band --- strain wave --- time window --- structural steel members --- steel frame --- acoustoelastic effect --- demand-based nodes --- online wayside detection --- sensor --- bridge --- sensitivity --- acoustic emission --- bending stiffness --- wireless smart sensors --- distributed dense sensor network --- mapping construction --- data fusion --- fuzzy classification --- shear-wave birefringence --- normal mode --- piezoelectric wafer active sensors --- resonant frequency --- electromagnetic oscillation --- settlement --- sensor optimization --- modal frequencies --- perturbation theory --- feature extraction --- Virtual Distortion Method (VDM) --- reflective optical sensor --- fibre bundle --- smartphones --- crack identification --- active sensing --- test vehicle --- calibration --- stretching method --- model updating --- rotary ultrasonic array --- 2D crack growth --- data processing --- damage detection --- impactor stiffness --- tip clearance --- transmission tower --- phase spectrum --- concrete crack --- structural impact monitoring --- shaking table tests --- optical crack growth sensor --- steel strand --- passive sensing --- sudden event monitoring --- aero engine --- principal component analysis

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The built environment of the future will implement innovative solutions for energy efficiency, self-monitoring infrastructures, and self-healing strategies, just to mention the most promising smart material technologies that will most likely assume a pivotal importance in the future. This book is a result of a very multidisciplinary effort aimed at driving the scientific progress of the constructions of the future. The works included in this book concern new materials and coatings for cultural heritage preservation, environmentally sustainable concretes and paving material components, self-healing, and durability strategies.

Smart materials.

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The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of "big data" availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.

Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- programmeren (informatica) --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie)