Roles of artificial intelligence in construction engineering and management: A critical review and future trends

Computer vision-based concrete crack detection using U-net fully convolutional networks

Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to t...

A review of computer vision–based structural health monitoring at local and global levels:

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review.

pdf/artificial-intelligence-and-smart-vision-for-building-and-1z4v5tr5.pdf

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

Autonomous concrete crack detection using deep fully convolutional neural network

A common practice for the manufacture of orthotropic steel decks in Japan is to use 75% partial joint penetration welds between closed ribs and deck plates. To evaluate the effectiveness of the proposed 100% penetration on improving the fatigue strength of rib-to-deck welded joints in orthotropic steel bridge decks, four full-scale orthotropic deck specimens were subjected to laboratory testing. Specimens, consisting of a 12-mm-thick deck plate and 6-mm-thick rib, with one closed rib, were fabricated with 75% and 100% penetration. Fatigue test results showed that fatigue cracks initiated from the weld toe inside the rib in the 100% penetration specimens, but from weld root inside the rib in the 75% penetration specimens. To investigate this fatigue behavior, strain measurements were taken at 5 mm from the rib-to-deck weld line. Results of finite element analysis using the effective notch stress method indicate that a deeper partial penetration results in a slightly higher effective notch stress at the weld root of the partial penetration weld. The effective notch stress at the crack initiation location with 100% penetration is lower than that obtained with partial penetration. The open angle appears to have a significant effect on the effective notch stress at the upper weld toe when using 100% penetration. Therefore, the proposed 100% penetration appeared to have a positive effect on enhancing the fatigue resistance of rib-to-deck welded joints.

Investigations on the Effect of Weld Penetration on Fatigue Strength of Rib-to-Deck Welded Joints in Orthotropic Steel Decks

A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks

Polyvinylidene Flouride (PVDF) is a film-type polymer that has been used as sensors and actuators in various applications due to its mechanical toughness, flexibility, and low density. A PVDF sensor typically covers an area of the host structure over which mechanical stress/strain is averaged and converted to electrical energy. This study investigates the fundamental "stress-averaging" mechanism for dynamic strain sensing in the in-plane mode. A numerical simulation was conducted to simulate the "stress-averaging" mechanism of a PVDF sensor attached on a cantilever beam subjected to an impact loading, taking into account the contribution of piezoelectricity, the cantilever beam's modal properties, and electronic signal conditioning. Impact tests and FEM analysis were also carried out to verify the numerical simulation results. The results of impact tests indicate the excellent capability of the attached PVDF sensor in capturing the fundamental natural frequencies of the cantilever beam. There is a good agreement between the PVDF sensor's output voltage predicted by the numerical simulation and that obtained in the impact tests. Parametric studies were conducted to investigate the effects of sensor size and sensor position and it is shown that a larger sensor tends to generate higher output voltage than a smaller one at the same location. However, the effect of sensor location seems to be more significant for larger sensors due to the cancelling problem. Overall, PVDF sensors exhibit excellent sensing capability for in-plane dynamic strain induced by impact loading.

Numerical Simulation of Output Response of PVDF Sensor Attached on a Cantilever Beam Subjected to Impact Loading

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Papers

Autonomous concrete crack detection using deep fully convolutional neural network

Investigations on the Effect of Weld Penetration on Fatigue Strength of Rib-to-Deck Welded Joints in Orthotropic Steel Decks

A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks

Numerical Simulation of Output Response of PVDF Sensor Attached on a Cantilever Beam Subjected to Impact Loading