Road Design Layer Detection in Point Cloud Data for Construction Progress Monitoring

Poor performance in transportation construction is well-documented, with an estimated $114.3 billion in global annual cost overrun. Studies aimed at identifying the causes highlighted traditional project management functions like progress monitoring as the most important contributing factors. Current methods for monitoring progress on road construction sites are not accurate, consistent, reliable, or timely enough to enable effective project control decisions. Automating this process can address these inefficiencies. The detection of layered design surfaces in digital as-built data is an essential step in this automation. A number of recent studies, mostly focused on structural building elements, aimed to accomplish similar detection but the methods proposed are either ill-suited for transportation projects or require labelled as-built data that can be costly and time consuming to produce. This paper proposes and experimentally validates a model-guided hierarchical space partitioning data structure for accomplishing this detection in discrete regions of 3D as-built data. The proposed solution achieved an F1 Score of 95.2% on real-world data confirming the suitability of this approach.This research is made possible through funding from the United States Air Force and the Cambridge Commonwealth and International Trust. The authors express gratitude to the Trimble Corporation for their support in lending equipment and expertise to the data collection operation

Choline intake and genetic polymorphisms influence choline metabolite concentrations in human breast milk and plasma

Background: Choline is essential for infant nutrition, and breast milk is a rich source of this nutrient. Common single nucleotide polymorphisms (SNPs) change dietary requirements for choline intake

Co-ordinated Airborne Studies in the Tropics (CAST)

This is the author accepted manuscript. The final version is available from the American Meteorological Society via http://dx.doi.org/10.1175/BAMS-D-14-00290.1The Co-ordinated Airborne Studies in the Tropics (CAST) project is studying the chemical composition of the atmosphere in the Tropical Warm Pool region to improve understanding of trace gas transport in convection. The main field activities of the CAST (Co-ordinated Airborne Studies in the Tropics) campaign took place in the West Pacific in January/February 2014. The field campaign was based in Guam (13.5°N, 144.8°E) using the UK FAAM BAe-146 atmospheric research aircraft and was coordinated with the ATTREX project with the unmanned Global Hawk and the CONTRAST campaign with the Gulfstream V aircraft. Together, the three aircraft were able to make detailed measurements of atmospheric structure and composition from the ocean surface to 20 km. These measurements are providing new information about the processes influencing halogen and ozone levels in the tropical West Pacific as well as the importance of trace gas transport in convection for the upper troposphere and stratosphere. The FAAM aircraft made a total of 25 flights between 1°S-14°N and 130°-155°E. It was used to sample at altitudes below 8 km with much of the time spent in the marine boundary layer. It measured a range of chemical species, and sampled extensively within the region of main inflow into the strong West Pacific convection. The CAST team also made ground-based measurements of a number of species (including daily ozonesondes) at the Atmospheric Radiation Measurement program site on Manus Island, Papua New Guinea (2.1°S, 147.4°E). This article presents an overview of the CAST project focussing on the design and operation of the West Pacific experiment. It additionally discusses some new developments in CAST, including flights of new instruments on the Global Hawk in February/March 2015.CAST is funded by NERC and STFC, with grant NE/ I030054/1 (lead award), NE/J006262/1, NE/J006238/1, NE/J006181/1, NE/J006211/1, NE/J006061/1, NE/J006157/1, NE/J006203/1, NE/J00619X/1, and NE/J006173/1. N. R. P. Harris was supported by a NERC Advanced Research Fellowship (NE/G014655/1). P. I. Palmer acknowledges his Royal Society Wolfson Research Merit Award. The BAe-146-301 Atmospheric Research Aircraft is flown by Directflight Ltd and managed by the Facility for Airborne Atmospheric Measurements, which is a joint entity of the Natural Environment Research Council and the Met Office. The authors thank the staff at FAAM, Directflight and Avalon Aero who worked so hard toward the success of the aircraft deployment in Guam, especially for their untiring efforts when spending an unforeseen 9 days in Chuuk. We thank the local staff at Chuuk and Palau, as well as the authorities in the Federated States of Micronesia for their help in facilitating our research flights. Special thanks go to the personnel associated with the ARM facility at Manus, Papua New Guinea without whose help the ground-based measurements would not have been possible. Thanks to the British Atmospheric Data Centre (BADC) for hosting our data and the NCAS Atmospheric Measurement Facility for providing the radiosonde and ground-based ozone equipment. Chlorophyll-a data used in Figure 1 were extracted using the Giovanni online data system, maintained by the NASA GES DISC. We also acknowledge the MODIS mission scientists and associated NASA personnel for the production of this data set. Finally we thank many individual associated with the ATTREX and CONTRAST campaigns for their help in the logistical planning, and we would like to single out Jim Bresch for his excellent and freely provided meteorological advice

Target selection and annotation for the structural genomics of the amidohydrolase and enolase superfamilies

To study the substrate specificity of enzymes, we use the amidohydrolase and enolase superfamilies as model systems; members of these superfamilies share a common TIM barrel fold and catalyze a wide range of chemical reactions. Here, we describe a collaboration between the Enzyme Specificity Consortium (ENSPEC) and the New York SGX Research Center for Structural Genomics (NYSGXRC) that aims to maximize the structural coverage of the amidohydrolase and enolase superfamilies. Using sequence- and structure-based protein comparisons, we first selected 535 target proteins from a variety of genomes for high-throughput structure determination by X-ray crystallography; 63 of these targets were not previously annotated as superfamily members. To date, 20 unique amidohydrolase and 41 unique enolase structures have been determined, increasing the fraction of sequences in the two superfamilies that can be modeled based on at least 30% sequence identity from 45% to 73%. We present case studies of proteins related to uronate isomerase (an amidohydrolase superfamily member) and mandelate racemase (an enolase superfamily member), to illustrate how this structure-focused approach can be used to generate hypotheses about sequence–structure–function relationships

Automated Spatial Progress Monitoring for Asphalt Road Construction Projects

Construction progress monitoring allows schedule and/or cost deviations to be identified early enough to effectively implement corrective actions. At least 77% of transportation projects experience cost overruns, and as much as 75% of these overruns have been attributed to “real” construction management factors like progress monitoring. Progress is measured on road construction sites in terms of completion percentages at various activity and work package levels. This percentage is then used to identify schedule deviations and support the earned value analysis often used as the baseline for contractor progress payments. Unfortunately, the current methods for producing these completion percentages are not as correct or time efficient as they should be to enable effective project control. The objective of this research is to develop, test, and validate a novel solution for automatically producing completion percentages and progress status determinations that are more correct and time efficient than those generated in current practice. The proposed solution seeks to automatically detect incremental progress on road design layers in 3D as-built point cloud data generated using unmanned aerial photogrammetry and a novel data simulation approach. A parallel as-planned progress estimate is also automatically prepared using 4D information, and the progress status determinations are made by comparing the two results. This solution was tested on 15 datasets (13 simulated and 2 real-world) representing a variety of road designs and progress conditions. The method achieved an average 95% F1 score in layer detection on the real-world data, and mostly outperformed current practice in correctness. The automated processing of as-built and as-planned data to produce the progress estimate took 12 seconds for the real world data, which was indeed faster than the current practice equivalent. Although the research objectives were met, there remains room for further improvement, particularly in regards to the solution’s robustness to occlusions on the monitored surfaces.United States Air Force Cambridge Trus

Planning, Design, and Analysis of Tailings Dams

The disposal of mine waste, chiefly tailings, has of late assumed an importance that transcends even the massive volumes of materials produced annually by mining operations. From an engineering standpoint, some tailings embankments class among the largest earth structures in the world. Aside from their significance in strictly engineering terms, tailings impoundments receive intense regulatory attention and public scrutiny. Because of the land areas they disturb and the varying toxicities of the mine wastes they retain, tailings impoundments are often the lightning rod for public opposition to mining projects. Historically, tailings disposal began as the practice of dumping tailings into nearby streams and progressed to empirical design of impoundments by mine operators based on less than satisfactory principles of trial and error. Only within about the past 20 years have the principles of geotechnical engineering been applied to tailings embankments, ordinarily in the context of design practices for water-retention dams. Now, however, planning and design of tailings impoundments has become a multidisciplinary enterprise, one that requires a broader background in many diverse fields extending beyond the traditional application of geotechnical knowledge. This book is intended to provide a bridge between the various technical disciplines involved in tailings disposal and to illustrate the application of these fields to tailings disposal planning and design. In addition, the intent is to provide the reader with access to key sources of literature applying to tailings that heretofore have been scattered among various conferences, symposia, and journals in a wide range of technical fields.Non UBCUnreviewedResearche

A Review of Linear Transportation Construction Progress Monitoring Techniques

Effective construction progress monitoring allows schedule and/or cost deviations to be identified early enough to implement corrective actions and avoid contract disputes. Current approaches are not accurate, consistent, reliable, or timely enough to enable effective project control decisions. This is, in part, the reason for an estimated $82.6 billion in global transportation project cost overruns each year. Recent studies have leveraged 4D Building Information Models (BIM) to automate the detection of as-planned components in as-built data; a crucial step in automating progress monitoring. However, the scope of these studies has been limited to mostly building construction components at the major activity level (e.g. columns, beams, foundations, walls, etc.). This paper presents a qualitative synthesis of the state-of-the-art in automated progress monitoring practice and research, focusing on approaches relevant to linear transportation projects. The literature is grouped into four broad categories: (1) image processing methods, (2) point cloud processing methods, (3) indirect methods, and (4) transportation and earthwork specific studies. Gaps in knowledge are identified for future research opportunities. It is concluded that more research is needed to proposefeasible methods for automating progress monitoring on transportation projects

Asphalt Road Layer Detection for Construction Progress Monitoring

Transportation construction projects consistently underperform, with an estimated $82.6 billion globally in annual cost overruns. Logistical challenges associated with the size and location of transportation construction sites is a contributing factor, as is the inefficiency of current progress monitoring practice. A method that leverages the rich 3D information available in Civil Infrastructure Models (CIMs) and accurate 3D reality capture technologies, like LIDAR or photogrammetry, could address these shortfalls. An essential task in implementing such a progress monitoring approach is the detection of relatively thin design surface layers in 3D as-built data. This paper proposes a method for accomplishing this detection and presents experimental results on as-built data collected during the construction of a small residential road in Cambridge, UK. A total of 640 experiments were run for different combinations of parameters and classification rules, producing a peak accuracy of 86.62%, peak precision of 80.65%, and peak recall of 92.50%. The most balanced combination of parameters and classification rule produced an accuracy of 86.50%, precision of 68.17%, and recall of 60.99%