Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …

Structural health monitoring (SHM) and damage assessment of civil engineering
infrastructure are complex tasks. Structural health and strength of structures are influenced …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

This paper reviews recent advances in sensor technologies for non-destructive testing
(NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by …

Bridges play a crucial role in modern societies, regardless of their culture, geographical
location, or economic development. The safest, economical, and most resilient bridges are …

Population growth, economic development, and rapid urbanization in many areas have led
to increased exposure and vulnerability of structural and infrastructure systems to hazards …

Deep learning has been widely applied to vision‐based structural damage detection, but its
computational demand is high. To avoid this computational burden, a novel crack detection …

A novel hybrid framework of optimized deep learning models combined with multi-sensor
fusion is developed for condition diagnosis of concrete arch beam. The vibration responses …

In recent decades, structural health monitoring (SHM) has gained increased importance for
ensuring the sustainability and serviceability of large and complex structures. To design an …

Technology advancement demands energy storage devices (ESD) and systems (ESS) with
better performance, longer life, higher reliability, and smarter management strategy …