Optimizing Maintenance Activities Second Report: Snow and Ice Control Operations

FH-11-8250This report presents the major results of an in-depth study of Snow and Ice Control Operations conducted in four States during the winter of 1975-76. The states involved were California, Colorado, Pennsylvania, and Utah. To the extent possible, within the time frame of the study, all aspects of snow and ice control activities (material, equipment and labor) were analyzed. Initial estimates indicate potential savings on the order of $5 million among the four states upon implementation of the changes recommended as a result of this project. The recommendation with the greatest potential for immediate cost reduction are the control of application rates and the adoption of ground control spreaders

New and Innovative Methods and Materials for Pavement Skid Resistance

DOT-FH-11-8480The report describes an evaluation and classification of pavement surfaces with respect to skid resistance. The study was conducted by means of a questionnaire survey of agencies within and adjacent to California, and by testing and examination of 45 existing pavement surfaces. The test program included standard skid tests at two speeds and additional tests with a smooth tire at one speed. Surface textures were measured by stereophotographs to obtain a "texture profile". The approximate cost of the surface, the amount of traffic exposure, and vehicle accident data were included in the evaluation. The pavement surfaces were ranked on the basis of skid number, speed gradient, and texture. Systems which ranked well under heavy or medium traffic included open-graded asphalt concretes with and without epoxy modification, textured cement concretes, and epoxy chip seals. Conventional and rubberized chip seals were found suitable for medium or light traffic. Dense graded epoxy-asphalt concretes generally ranked about the same as the control section of asphalt concrete. The corrective surface treatments considered new and innovative were all quite expensive compared to conventional treatments. Wet pavement accidents data did not provide any criteria for establishing minimum levels of skid resistance

Life Cycle Assessment With Primary Data on Heavy Rare Earth Oxides From Ion-Adsorption Clays

This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: Deng, H. & Kendall, A. Int J Life Cycle Assess (2019) 24: 1643. https://doi.org/10.1007/s11367-019-01582-1Heavy and light rare earth elements (REEs) are critical to clean energy technologies, and thus the environmental impacts from their production are increasingly scrutinized. Most previous LCAs of REE production focus on sites producing light REEs. This research addresses this gap by collecting primary data from sites producing heavy rare earth oxides (HREOs) from ion-adsorption clays, conducting an LCA, and providing open-source life cycle inventory (LCI) datasets of HREO production for the LCA community

Improved California Truck Traffic Census Reporting and Spatial Activity Measurement

UC-ITS-2019-37The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation operational and planning needs. Many transportation agencies rely on Weigh-In-Motion and automatic vehicle classification sites to collect vehicle classification count data. However, these systems are not widely deployed due to high installation and operations costs. One cost-effective approach investigated by researchers has been the use of single inductive loop sensors as an alternative to obtain FHWA vehicle classification data. However, most models do not accurately classify under-represented classes, even though many of these minority classes pose disproportionally adverse impacts on pavement infrastructure and the environment. As a consequence, previous models have not been able to adequately classify under-represented classes, and the overall performance of the models are often masked by excellent classification accuracy of the majority classes, such as passenger vehicles and five-axle tractor trailers. This project developed a bootstrap aggregating (bagging) deep neural network (DNN) model on a truck-focused dataset obtained from Truck Activity Monitoring System (TAMS) sites, which leverage existing inductive loop sensor infrastructure coupled with deployed inductive loop signature technology, and already deployed statewide at over ninety locations across all Caltrans Districts. The proposed method significantly improved the model performance on truck-related classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research studies. Remarkably, the proposed model is also capable of distinguishing classes with overlapping axle configuration, which is generally a challenge for axle-based sensor systems

The Effects of Long-Duration Subduction Earthquakes on Inelastic Behavior of Bridge Pile Foundations Subjected to Liquefaction-Induced Lateral Spreading

Effective-stress nonlinear dynamic analyses (NDA) were performed for a large-diameter reinforced concrete (RC) pile in multi-layered liquefiable sloped ground. The objective was to assess the effects of earthquake duration on the combination of inertia and liquefaction-induced lateral spreading. A parametric study was performed using input motions from subduction and crustal earthquakes covering a wide range of motion durations. The NDA results showed that the pile head displacements increased under liquefied conditions, compared to nonliquefied conditions, due to liquefaction-induced lateral spreading. The NDA results were used to develop a displacement-based equivalent static analysis (ESA) method that combines inertial and lateral spreading loads for estimating elastic and inelastic pile demands

Smart Sensing System for Real-time Automatic Traffic Analysis of Highway Rest Areas

State transportation agency spends millions of dollars annually to maintain and improve the service provided to the drivers in the highway rest areas. In order to collect traffic data in real-time, Researchers can use the vehicle data in the rest areas. Therefore, it is helpful immensely to update the existing safety policies in the rest areas. Transportation agencies don\u2019t have any automated systems to perform \u201cautomatic\u201d and \u201creal-time\u201d vehicle identification and classification in the highway rest areas. Motivated by a dire need to enhance and modernize the transportation system, the author proposes an advanced modular system that will integrate a smart sensor to extract a rest area traffic pattern in real-time. Currently, Caltrans collects traffic data from Automated Vehicle Classification (AVC) stations and also manual census collected in the specific locations. However, this technology is too expensive, time consuming, and disruptive; therefore it has not been used widely in many different locations. In recent years, There have been many significant improvements in MEMS sensors domain with respect to size, cost and accuracy. Moreover, extreme miniaturization of RF transceivers and low power micro-controllers have motivated researchers to develop small and low power sensors and radio equipped modules. These sensors are gradually replacing traditional wired sensor systems. These modules which are often called \u201csensor mote\u201d (size of a quarter) communicate with other sensor nodes and build an intelligent network of sensors. Because of the miniaturization and low power consumption, these sensor motes are extremely efficient due to their low power budget. The authors propose a wireless MEMS sensor based automatic vehicle classification and identification system for highways rest areas. The author's developed Automatic Vehicle Classification and Identification (AVCI) system consists of two parts, AVCI sensor nodes containing magneto-resistive and accelerometer sensors. These sensors calculate speed and axles respectively. The next part, the system proposes a Access Point (AP) which collects data from sensor motes and calculate speed, axles counts and then it classifies the collected data based on Federal Highway Administration (FHWA) 13-categories Scheme-F[5]. The AP includes a RF transceiver to communicate with the sensor motes and also a GPRS (General Packet Radio Service) shield to transmit aggregated traffic data to the county or regional traffic data collection center