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ارزیابی پتانسیل ریزش ترانشه جاده های جنگلی (مطالعه موردی: حوضه آبخیز 46، شمال ایران) | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 17، دوره 23، شماره 3، مهر 1394، صفحه 573-582 اصل مقاله (276.86 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2015.105661 | ||
| نویسندگان | ||
| زهرا عزیزی* 1؛ اصغر حسینی2 | ||
| 1استادیار، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد | ||
| 2دانشجوی کارشناسی ارشد جنگلداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد | ||
| چکیده | ||
| شبکه جاده‌ها اساس تولید مدرن و پایدار جنگل‌های هیرکانی هستند. این جنگل‌ها اغلب در مناطق پرشیب و کوهستانی واقع شده‌اند، بنابراین پس از ساخت جاده، همواره احتمال وقوع ریزش ترانشه‌های جاده وجود دارد. ارزیابی میزان این احتمال در زمان طراحی و ساخت جاده‌ها از اهمیت بسزایی برخوردار است. در پژوهش پیش‌رو با استفاده از مدل شبکه عصبی مصنوعی ریزش ترانشه‌های جاده مورد ارزیابی قرار گرفت و احتمال وقوع ریزش ترانشه در چهار طبقه بدون ریزش، ریزش کم، ریزش متوسط و ریزش زیاد دسته‌بندی شد. نتایج نشان داد که ریزش کم با 39/8 درصد بیشترین احتمال وقوع و ریزش زیاد (31/3 درصد) کمترین احتمال وقوع را داشته‌اند. این مدل در پیش‌بینی احتمال وقوع ریزش متوسط و عدم وقوع ریزش به‌ترتیب کمترین (78/6 درصد) و بیشترین صحت (92/6 درصد) داشت. بررسی عامل‌های مؤثر در احتمال وقوع ریزش ترانشه‌ها نشان داد که به‌ترتیب زاویه ترانشه نسبت به سطح افق، شیب طبیعی عرصه و ارتفاع ترانشه بیشترین تأثیر و جنس خاک و تعداد درخت تا شعاع 10 متری محل اندازه‌گیری ریزش، کمترین تأثیر را در وقوع ریزش داشته‌اند. | ||
| کلیدواژه‌ها | ||
| جاده جنگلی؛ ریزش ترانشه؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Evaluation of slope failure potential in forest roads (Case study: 46th watershed, North of Iran) | ||
| نویسندگان [English] | ||
| Zahra Azizi1؛ Asghar Hosseini2 | ||
| 1Assistant Prof., Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran | ||
| 2M.Sc. Forestry, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran | ||
| چکیده [English] | ||
| Forest road networks are the essential bases of modern and sustainable production within Hyrcanian forests of Iran. In most mountainous areas, the slope failures are often caused by the construction of forest road on steep terrains. Therefore, it is crucial to locate forest roads on stable slopes. It should be additionally noted that high-structured forest roads constructed on steep terrain often end up with failure during cut and fill operations. In this study, a model was developed using the Artificial Neural Network to evaluate potential failure of cut and fill in forest. Probability of slope failure was classified in four categories, including no, low, medium and high slope failures. The overall result showed that low slope failure category (with a probability of 39.8%) has high occurrences, whereas high slope failure category (with probability of 13.3%) was associated with fewer occurrences. As modelled by ANN, lowest and highest accuracies for classified slope failure were 92.6% (in no slope failure category) and 78.6% (in medium slope failure category) respectively. Based on importance indices, gradient of trench, natural slope of area and the height of the trench were associated with the greatest influences on the slope failure, while the soil type and number of trees turned out to be of least effect on the slope failure occurrence. | ||
| کلیدواژه‌ها [English] | ||
| forest road, slope failure, Artificial Neural Network | ||
| مراجع | ||
|
- Akay, A.E., 2006. Minimizing total costs of forest roads with computer-aided design model. Sadhana-Academy Proceedings in Engineering Sciences, 31(5): 621-633. - Anonymous., 2012. Guideline for the Design, Construction and Operation of Forest Roads. President deputy Strategic Planning and Control, No.131, 159p. - Azizi, Z. and Najafi, A., 2011. Fuzzy classification in forest area for road design (Case study: Lirehsar forest, Tonekabon). Iranian Journal of Forest and Poplar Research, 19(1): 42-54 (In Persian). - Baloochian, H. and Baloochian, S., 2012. Predictive modeling of traffic and speed in roads using artificial neural network. Rahvar Journal, 19: 51-60 (In Persian). - Bhasin, R. and Kaynia, A.M., 2004. Static and dynamic simulation of a 700-m high rock slope in western Norway. Engineering Geology, 71(3): 213-226. - Borga, M., Tonelli, F. and Selleroni, J., 2004. A physically based model of the effects of forest roads on slope stability. Water Resources Research Journal, 40(12): 1-9. - Brown, G.W. and Krygier, J.T., 1971. Clear-cut logging and sediment production in the Oregon Coast range. Water Resources Research, 7(5): 1189-98. - Duncan, M., Quinn, T. and Tremaine, S., 1987. The formation and extent of the solar system comet cloud. Astronomical Journal, 94: 1330-1338. - Dyrness, C.T., 1967. Mass soil movements in the H.J. Andrews Experimental Forest. USDA Forest Service, Pacific Northwest Forest and Range Experiment Station Research, 42p. - Furbish, D.J. and Rice, R.M., 1983. Predicting landslides related to clear-cut logging, Northwestern California, USA. Mountain Research and Development, 3(3): 253-259. - Gucinski, H., Furniss, M.J., Ziemer, R.R. and Brookes, M.H., 2001. Forest roads: A synthesis of scientific information. General Technical Report PNW-GTR-509, U.S. Pacific Northwest Research Station, 103p. - Hecht-nielsen, R., 1987. Kolmogorov mapping neural network existence theorem. Proceedings of the First IEEE International Conference on Neural Networks, pp. 11-14. - Hosseini, S.A., Khalili, M.E., Pourmajidian, M., Fallah, A. and Parsakhoo, A., 2012. Relationship between forest road length and village development during three decades in Neka-Zalemrood forest. Journal of Forest Science, 58(1): 1-7. - Jeng, D.S., Lee, T.L. and Lin, C., 2003. Assessment of Chi-Chi earthquake-induced liquefaction; Application of ANN model. Proceedings of 7th Conference on Artificial Intelligence and Applications, 50p. - Kainthola, A., Singh, P.K., Wasnik, A.B. and Singh, T.N., 2012a. Distinct element modeling of Mahabaleshwar road cut hill slope, Geometerials, 2: 105-113. - Kainthola, A., Singh, P.K., Wasnik, A.B. and Singh, T.N., 2012b. Finite element analysis of road cut slopes using Hoek and Brown failure criterion. International Journal of Earth Sciences and Engineering, 5(5): 1100-1109. - Keller, K.L., 1993. Strategic brand management: Building, measuring, and managing brand equity. Journal of Marketing, 57(1): 1-22. - Lee, T., Lin, H. and Lu, Y., 2009. Assessment of highway slope failure using neural networks. Journal of Zhejiang University Science, 10(1): 101-108. - Mcnutt, N.S., 1975. Ultrastructure of the myocardial sarcolemma. Journal of the American Heart Association, 37(1): 1-13. - Nekooimehr, M., Rafatnia, N., Raisian, S., Jahanbazi, H., Talebi, M. and Abdolahi, Kh., 2006. Impact of road construction on forest destruction in Bazoft region. Iranian Journal of Forest and Poplar Research, 14 (3): 228-243 (In Persian). - Rice, R.M. and Pillsbury, N.H., 1982. Predicting landslides in clear-cut patches. International Association of Hydrological Sciences, 137: 303-311. - Shafabakhsh, G.A., Fathi, F. and Zayerzadeh, A., 2010. Prioritization of eventful roads correction using artificial neural networks. Journal of Modeling in Engineering, 8(20): 71-82 (In Persian). - Soori, S., Lashkaripour, G.R. and Ghafoori, M., 2012. Landslide hazard zonation using artificial neural network. Journal of Engineering Geology, 5(2): 1269-1286. - Souley, M and Homand, F., 1996. Stability of jointed rock masses evaluated by UDEC with an extended Seab Amadei constitutive law. International Journal of Rock Mechanics and Mining Sciences, 33(3): 233-244. - Stuckelberger, J.A., 2008. A Weighted-graph Optimization Approach for Automatic Location of Forest Road Network. Hochschulverlag AG, Zurich, 127p. - Swanston, D.N. and Dyrness, C.T., 1973. Stability of steep land. Journal of Forestry, 71: 264-269. - Swanston, D.N., Swansona, F.J. and Rosgen, D., 1979. Prediction techniques for determining potential changes in sediment contribution from mass wasting processes due to silvicultural activities. In: Water Resources Evaluation, Non-point Sources, Silviculture (A Procedural Handbook), 600p. -Watson, A.J, and Marden, M., 2004. Live root-wood tensile strengths of some common New Zealand indigenous and plantation tree species. New Zealand Journal of Forestry Science 34(3): 344-353. - Yang, Y. and Zhang, Q., 1998. The application of neural network to rock engineering systems (RES). International Journal of Rock Mechanics and Mining Sciences, 35(6): 727-745. - Yoo, B.S., Park,Y.D., Lee, K.S., Kim, N.K. and Pyeon, M.W., 2009. A study on USN management for tunnel entry slope. Journal of Convergence Information Technology, 4(2): 1-4. - Yoshimura, T., 1997. Development of an Expert System Planning a Forest Road Based on the Risk Assessment. Kyoto University, Kyoto: 3-35. - Yousefi, S., Tazeh, M., Mirzaee, S., Moradi, H.R. and Tavangar, Sh., 2011. Comparison of different classification algorithms in satellite imagery to produce land use maps (Case study: Noor city). Journal of Applied RS & GIS Techniques in Natural Resource Science, 2(2): 15-25 (In Persian). | ||
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