A novel machine learning‐based algorithm to detect damage in high‐rise building structures

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A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, neutral): “For example, in structural engineering, ML has been applied for structural system identification [57], seismic performance assessment [58], modeling of compressive [59] and tensile [60] concrete strength, shear capacity estimation of FRP-reinforced concrete beams [61], vibration control [62], as wel”
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review (2020, background): “Accordingly, several SHM systems have emerged and been implemented in bridges [2], high-rise buildings [98], towers [89], dams [91], tunnels [80] and so forth.”
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, methodology): “Besides, the restricted Boltzmann machine has been applied to structural health monitoring (Raﬁei & Adeli, 2017, 2018; Raﬁei, Khushefati, Demirboga, & Adeli, 2017).”
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network (2018, background): “According to the morphological and positional features of cracks, the intrinsic damage, deterioration, and the potential causes can be inferred, which provides reasonable guidance on structure assessment (Raﬁei and Adeli, 2017; Raﬁei et al., 2017).”