A review of recent strategies for acid mine drainage prevention and mine tailings recycling.

This chapter describes the methodology used in EPA’s Waste Reduction Model (WARM) to estimate streamlined life-cycle greenhouse gas (GHG) emission factors for concrete beginning at the point of waste generation. The WARM GHG emission factors are used to compare the net emissions associated with concrete in the following two waste management alternatives: recycling and landfilling. Exhibit 1 shows the general outline of materials management pathways for concrete in WARM. For background information on the general purpose and function of WARM emission factors, see the Introduction & Overview chapter. For more information on Recycling and Landfilling, see the chapters devoted to these processes. WARM also allows users to calculate results in terms of energy, rather than GHGs. The energy results are calculated using the same methodology described here but with slight adjustments, as explained in the Energy Impacts chapter.

나의 Concrete 연구실

Encoder–decoder network for pixel-level road crack detection in black-box images

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.

Utilization of steel slag as aggregate in asphalt mixtures for microwave deicing

Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks

Crack length measurement is an important part of asphalt pavement detection. However, some crack measurement techniques cannot satisfy the needs of accuracy and efficiency. This study discusses app...

Jie Gao

Papers

Utilization of steel slag as aggregate in asphalt mixtures for microwave deicing

Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks

Recognition of asphalt pavement crack length using deep convolutional neural networks

Microwave deicing for asphalt mixture containing steel wool fibers

Structural characterization and antioxidant activity of a novel high-molecular-weight polysaccharide from Ziziphus Jujuba cv. Muzao