Over the last two decades, the integration of big data and deep learning technologies has
demonstrated remarkable effectiveness across various domains of civil engineering, leading …

Significant developments in digital technologies can potentially provide managers and
engineers with the ability to improve the quality of the construction industry. Acknowledging …

Recent advancements in robotics and deep learning (DL) have made it possible to
implement robots in civil infrastructures' quality defect inspection. Robots can reduce human …

This paper presents a deep learning-based approach for multiclass surface damage
detection and segmentation in various bridge components. The proposed BridgeNet …

Cracks are a common defect in civil infrastructures, and their occurrence is often closely
related to structural loading conditions, material properties, design and construction, and …

Nowadays, the number of aging civil infrastructures is growing world-wide and when
concrete is involved, cracking and delamination can occur. Therefore, ensuring the safety …

Detecting damage in bridges can be an arduous task, fraught with challenges stemming
from the limitations of the inspection environment and the considerable time and resources …

Significant strides in deep learning for image recognition have expanded the potential of
visual data in assessing damage to reinforced concrete (RC) structures. Our study proposes …

Methods of structural health monitoring (SHM) are often challenged by changing
environmental and operational conditions (EOC). This paper presents a novel experimental …

Damage to reinforced concrete (RC) facilities occurs through the process of natural
deterioration. Machine learning can be employed to effectively identify various damage …