The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

American Society for Testing and Materials

The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

/pdf/the-environmental-protection-agency-3gx4opzrjm.pdf

The Environmental Protection Agency

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Dynamics of structures

This article proposes a deep neural network, namely CrackPropNet, to measure crack propagation on asphalt concrete (AC) specimens. It offers an accurate, flexible, efficient, and low-cost solution for crack propagation measurement using images collected during cracking tests. CrackPropNet significantly differs from traditional deep learning networks, as it involves learning to locate displacement field discontinuities by matching features at various locations in the reference and deformed images. An image library representing the diversified cracking behavior of AC was developed for supervised training. CrackPropNet achieved an optimal dataset scale F-1 of 0.755 and optimal image scale F-1 of 0.781 on the testing dataset at a running speed of 26 frame-per-second. Experiments demonstrated that low to medium-level Gaussian noises had a limited impact on the measurement accuracy of CrackPropNet. Moreover, the model showed promising generalization on fundamentally different images. As a crack measurement technique, the CrackPropNet can detect complex crack patterns accurately and efficiently in AC cracking tests. It can be applied to characterize the cracking phenomenon, evaluate AC cracking potential, validate test protocols, and verify theoretical models.

Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network

Water accelerates flexible pavement damage in various ways, including subgrade and granular pavement materials strength reduction, frost heave, swelling of expansive subgrade soils, and asphalt binder stripping of aggregate in asphalt concrete (AC) mixes. This paper presents case studies of AC pavement sections in the U.S. that withstood high moisture events with limited effect on pavement performance and no immediate need for major maintenance or rehabilitation actions. The case studies comprise pavement sections in Florida and Louisiana that were subjected to catastrophic flooding events, a pavement section in Delaware that is constantly exposed to tidal water level variations, and pavement sections in Montana and Minnesota that experience seasonal freeze-thaw. These cases illustrated that flexible pavements could withstand climatic changes when appropriately designed and constructed. Mechanically or chemically stabilized subgrade, proper drainage, and/or minimizing plastic fines in unbound layers are all techniques that could effectively control moisture damage.

Flexible Pavement Resiliency and Mitigation Strategies Following Adverse Environmental Events

Building durable and sustainable pavements

Life cycle assessment (LCA) and life cycle cost analysis (LCCA) are the main pillars of pavement sustainability LCA addresses environmental impacts of a pavement structure, and LCCA addresses life cycle costs While both techniques go hand in hand with transportation agencies’ decision making, they are usually used separately because there are no tools or methodologies that consider both under the same framework This study introduces a LCCA add-on to a LCA tool developed by the Illinois Center for Transportation (ICT) The tool analyzes all pavement life stages, namely, construction and materials, maintenance, use and end of life With the new add-on, the tool can calculate agency costs and user costs associated with both work zone and normal operating conditions as well as global warming potential and energy consumption The developed tool makes the pay item framework easier to use for agencies and contractors At the end, a case study is presented to illustrate the tool’s capabilities

Imad L. Al-Qadi

Papers

Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network

Flexible Pavement Resiliency and Mitigation Strategies Following Adverse Environmental Events

Building durable and sustainable pavements

Combined Life Cycle Cost Analysis and Life Cycle Assessment of Road Pavements

Application of FBG strain sensors in the measurement of three-directional strains within asphalt pavement