A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function

Ramin Ghiasi, Peyman Torkzadeh, Mohammad Noori

Structural Health Monitoring 2016 article cited by 169

Source More in Engineering & Materials

importance 5/10 ai assisted Structural Health Monitoring Techniques structural health monitoring stage: research

Problem

Detecting structural damage with high certainty from sensor measurements, to extend the life of engineered structures.

Why it matters

Reliable damage detection supports health assessment and extends the life of structures.

What the AI did

Proposed a least square support vector machine with a novel combinational kernel (thin plate spline radial basis plus modified Littlewood–Paley wavelet), with parameters optimized by a social harmony search algorithm.

Results

not stated in source

Impact

A new damage-detection strategy is proposed; the abstract truncates before any experimental results.

Record

Venue	Structural Health Monitoring
Published	2016
Work type	article
Authors	Ramin Ghiasi, Peyman Torkzadeh, Mohammad Noori
Citations	169 (OpenAlex, at import time)
References	39
Model or method	least square support vector machine (LS-SVM) with combinational kernel
Organization	not stated in source
Primary source	https://doi.org/10.1177/1475921716639587
Open access	closed
Keywords	support vector machine, structural damage detection, machine learning, kernel function, health monitoring

Importance rating

5 / 10

real but narrower advance

Damage detection with high certainty extends structure life; abstract calls health monitoring fast-growing but quantifies no beneficiaries or structures affected.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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