ANALYSIS OF PAVEMENT CONDITION INDEX DUE TO CLIMATE FACTORS FOR REGENCY ROAD
Bibliographic record
Abstract
Rodney van der Ree, Jochen A. G. Jaeger, Edgar A. van der Grift and Anthony P. Clevenger Effects of Roads and Traffic on Wildlife Populations and Landscape Function: Road Ecology is Moving toward Larger Scales (Mar 2011). \nGroup IPA. Pavement Asset Management Guidance Condition Surveying and Rating - Drainage. 2014;(December):1–13. \nReclamation USB. Drainage Manual. 2007;420. \nModul RDE 08: Traffic Engineerin 2005. \nDimitrios J., Maria F. Sustainable development variables to assess transport infrastructure in remote destination. 2016 \nAtlantis Highlights in Engineering (AHE), volume 1Copyright © 2018, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).International Conference on Science and Technology (ICST 2018 \nSholichin and A. Rumintang, “Relation analysis of road damage with excessive vehicles load on Kalianak road Surabaya,” J. Phys.: Conf. \nSer., vol. 953, pp. 012231_1- 012231_5, 2016. \nEvaluation and Maintenance of Road Damage in Sidotopo Surabaya Road Using Pavement Condition \nIndex (PCI) Method. (http://creativecommons.org/licenses/by-nc/4.0/).International Conference on Science and Technology (ICST 2018). \nM. Tariq, S.S. Pimplikar, “A Comparative Study on Pavement Condition Rating Methods for Flexible Roads,” IJEDR, vol. 5, pp. 2321, 2017. \nP. Babashamsi, N. Izzi, H. Ceylan and N. Ghani. ScienceDirect Evaluation of pavement life cycle cost analysis : Review and analysis. Int J Pavement Res Technol [Internet]. 2016;9:241–54. Retrieved from: \nFMA. Karim, KAH. Rubasi and AA. Saleh.The Road Pavement Condition Index (PCI) Evaluation and Maintenance: A Case Study of Yemen. Organ Technol Manage Constar an Int J [Internet]. 2016;8(1):1446–55. \nD. Hein and R. Burak. Development of a pavement condition rating procedure for interlocking concrete pavements. 2007 Annu Conf Transp Assoc Canada Trans - An Econ Enabler, TAC/ATC 2007. 2007;1–11. \nAO. Yisa, G. Lazhi and Paul D. Bad Drainage and Its Effects on Road Pavement Conditions in Nigeria. Civ Environ Res. 2013;3(10):7–16. \nMNU. Mia, T. Henning and S. Costello. Life cycle cost analysis to identify the need for drainage renewal in maintenance of road asset: Case Studies from a New Zealand road network. 9th Int Conf Manage Pavement Assets. 2015;5165 (Abstract 230). \nAmerican Association of State Highway and Transportation Officials, A Policy on Geometric Design of Highway and Streets, Washington DC, 1990. \nTransportation Research Board, National Research Council, Highway Capacity Manual, Special Report, Washington DC, 1985. \nKadiyali, L.R., Traffic Engineering and Transport Planning, Kanna Publisher, Delhi, 1978. \nDepartemen Pekerjaan Umum, Direktorat Jenderal Bina Marga, Manual Kapasitas Jalan Indonesia (MKJI), Jakarta, Februari 1997. \nDepartemen Pekerjaan Umum, Direktorat Jenderal Bina Marga, Tata Cara Perencanaan Geometrik Jalan Antar Kota, Jakarta, September 1997 \nP. Babashamsi, N. Izzi, H. Ceylan and N. Ghani. ScienceDirect Evaluation of pavement life cycle cost analysis : Review and analysis. Int J Pavement Res Technol [Internet]. 2
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".