Application of Soft Computing Techniques for Prediction of Slope Failure in Opencast Mines

One of the most arduous jobs in the industry is mining which involves risk at each working stage. Stability is the main focus and of utmost importance. FOS when calculated by traditional deterministic approach cannot represent the exact state at which the slope exists, though it gives a rough idea of the conditions and overall safety factor. Various approaches like numerical modelling, soft computing techniques allow us with the ease to find out the stability conditions of an unstable slope and the probability of its failure in near-by time. In this project, the stability conditions of some of the benches of Bhubaneswari Opencast Project, located in Talcher, have been evaluated using the soft-computing techniques like Artificial Neural Network implemented using MATLAB and then the results are being compared with the Numerical Model results from the software FLAC which deploys Finite Difference Method. A particular slope (CMTL-179, Seam-3) has been studied and the respective factor of safety for each slope has been predicted using both the Artificial Neural Network and FLAC. Initially the data related to bench height, slope angle, lithology, cohesion, internal angle of friction, etc. are determined for the respective rock of the slope of which the FOS is to be calculated. . A total of 14 training functions were used to train the model. The best training was found in Scaled Conjugate Gradient Backpropagation which corresponds to a regression coefficient of 91.36% during training and 88.24% overall. The best Validation Performance was also found at 60 epochs with Mean Squared Error of 0.069776. According to the trained neural network, it was found that the slope was 44.5% stable with a FOS 1.0226. Using the software FLAC, it was found that the slope was stable with FOS=1.17. The generic model will thus allow us to get a range of probability for the slope to fail so that necessary arrangements can be made to prevent the slope failure

Нейромережевий додаток визначення параметрів автоматичного регулятора

Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник проекту: доцент Глазок Олексій МихайловичСистема автоматичного регулювання (САР) – це така система автоматичного керування, задача якої полягає у підтримці на заданому рівні або зміні по заданому закону вихідної величини Y(t) об'єкта. Система автоматичного регулювання складається з регульованого об'єкта та елементів управління, які впливають на об'єкт при зміні однієї або декількох регульованих змінних. Під впливом вхідних сигналів (управління або обурення), змінюються регульовані змінні. Мета ж регулювання полягає у формуванні таких законів, при яких вихідні регульовані змінні мало відрізнялися б від необхідних значень. Рішення даного завдання в багатьох випадках ускладнюється наявністю випадкових збурень (перешкод). При цьому необхідно вибирати такий закон регулювання, при якому сигнали керування проходили б через систему з малими спотвореннями, а сигнали шуму практично не пропускалися

Hydro-geochemical modelling of an unlined landfill site

This project reports results of a hydro-geochemical study of leachate production at the Silent Valley landfill, Blaenau Gwent, South Wales, UK. Silent Valley landfill site is an active unlined landfill in South Wales. It lies on interbedded sandstones and mudstones of the Rhondda Beds, which are overlain by a mixture of boulder clay, head deposits and made ground. The annual rainfall recorded in the area is approximately 1250 mm. Resistivity surveys were performed across part of the site to help improve the understanding of the internal structure of the landfill. Instrumentation to measure leachate discharge, conductivity and meteorological inputs installed at the Silent Valley landfill site are described and relationships between the rainfall and discharge data are analysed. Regression analysis is used to model the discharge of leachate from the measured meteorological data. Water balance analysis has demonstrated that groundwater is entering the site. The leachate generated on site is collected by a series of drains that feed into the Settlement Tank, which then discharges to foul sewer. The discharge through the Settlement Tank shows a rapid response to rainfall events with dilution effects indicated by conductivity readings and chemical analysis. The volume of discharge from the Settlement Tank is shown to have a long-term upward trend. A preliminary study was undertaken to investigate the use of neural networks to model the discharge in the Settlement Tank. Feedforward backpropogation neural networks were constructed using the measured meteorological data to produce predictions of daily discharge for the Settlement Tank. Ion Chromatography analysis was performed on the leachate to complement the historical leachate analysis data. Element concentration was correlated with conductivity data and variations related to discharge measurements. Since monitoring began in 1993, many of the leachate constituents have shown an increase in concentration.EThOS - Electronic Theses Online ServiceGBUnited Kingdo