Abstract. Land subsidence phenomenon due to human activities and natural factors has been observed in the plains of Fars province, which imposes heavy damage to agricultural lands, rural buildings, and historical monuments. In this paper, after identifying and determining rate of damage in Fars province, Marvdasht plain and its damaged villages have been chosen as the case study. The land subsidence map of Marvdasht and villages was presented by the data obtained from state of Groundwater, soil, and soil parameters. Also, the e ects of faults, tectonic situation, dried qanat, occurred earthquakes in this area, and the observed ssured were taken into account. Generally, uncontrolled withdrawals from deep drilling wells caused subsidence and extended damage in other areas. Earthquakes intensi ed the damage to rural buildings and contributed to opening gaps. The altered subsidence identi ed in di erent parts of Marvdasht plain and the damage in rural areas along with the predictions made could represent the progressive damage of this phenomenon in the future. Finally, with respect to the climate, geology, land status, characteristic of soil layers, and regional potential, appropriate solutions for the land subsidence prevention and, consequently, reduction of the related damages were presented.
The potential risk of subsurface anomalies such as sinkhole and cavity has always been an important issue in geotechnical and geophysical engineering. Subsurface cavities have dissimilar effects on different components of Rayleigh wave in each direction. This paper intends to detect cavity and intrusion in the half-space and layered soil media. Rayleigh (R) wave propagation is analyzed according to the classical Multi-channel Analysis of Surface Waves (MASW) method. 2D and 3D simulations of Surface wave testing were conducted using Finite Element Modeling (FEM) in Abaqus 6.14. The results show the significant effect of the subsurface cavity on the particle motion and radial component of the Rayleigh wave. Cavity location is determined based on the variation of the maximum normalized amplitude for each trace. Furthermore, the perturbation of the elliptic shape above the cavity can help in cavity detection compared with the recorded data before and after the cavity.
Swelling in compacted soils may lead to some damages to structures and buildings. For the sake of reducing such damages, soil swelling should be determined, so as to make the structures exhibit adequate resistance against such a phenomenon. For most cases, fully non-linear relations have been observed between soil swelling and the parameters contributing to swelling in compacted soil. As such, soil swelling should be determined via either experimentations or prediction models. However, being extremely timely, swelling tests require special expensive equipment. Accordingly, there is a need for models which can use available data to theoretically give swelling estimations of a relatively high accuracy without getting busy with swelling tests and associated issues. Investigated and evaluated in this research are the ability and application of an adaptive neuro-fuzzy interference system (ANFIS) developed by subtractive clustering and fuzzy c-mean clustering to determine and predict swelling in compacted soils. The results along with the obtained values of root mean squared error (RMSE), mean absolute error (MAE) and coefficient of correlation (R) indicated that the proposed ANFIS model succeeded to predict swelling in compacted soils at a good level of accuracy. Therefore, ANFIS models can be used to predict swelling without getting busy with swelling tests and associated issues. KEYWORDS: Swelling of compacted soil, Subtractive clustering, Fuzzy c-mean clustering, ANFIS, Prediction.
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