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Complex engineering projects including large dams require extensive reconnaissance. The study of geological relationships is therefore of major importance, with emphasis on the characteristics of the geological structures. Accordingly, geologic structure affects dam site and reservoir behavior in three ways: (1) its impact on the geomechanical properties of rocks; (2) the importance of geologic structures in the identification and assessment of karst hydrogeology; and (3) its role in seismotectonic and seismic risk analysis of dam projects. Site geology and availability of various geologic data obtained from site investigation are key points in dam construction. Geological structure plays an important role in dam site geology and imposes major limitations on dam behavior during and after construction stages. This role has its own effect on major subjects such as: morphotectonics of rivers; geotechnical properties and engineering geology of dam sites; and hydrogeology of dam abutments and reservoir. The variability and complexity of geological structures regarding their tectonic situation result in different scenarios regarding dam's behavior. This chapter examines the link between geological structure and dam behavior during and after construction period by describing four dam case examples: two earth (Marun and Gotvand) and two concrete (Karun-1 and -3) dams in Iran.

Wind speed is the main driver of wind power output, but its inherent fluctuations and deviations present significant challenges for power system security and power quality. Accurate short-term wind power forecasting is necessary to ensure the stability and integration of wind energy into the grid. Non-stationarity is a major challenge in analyzing wind speed data, and change-point detection are essential for optimal resource allocation. This paper addresses the issue of short-term wind power forecasting for stable and effective wind energy system operation. To predict non-stationary data and detect change points, non-stationary data must first be transformed into stationary data. Discrete wavelet transformation (DWT) is used to decompose wind speed traces into low- and high-frequency components for more accurate predictions using deep learning algorithms. The proposed approach uses a Gated Recurrent Unit (GRU) network, which has a concise network structure and requires less computational load, making it suitable for quickly predicting short-term and long-term dependencies in wind speed data. Experiments demonstrate that the proposed method outperforms other cutting-edge methods in terms of prediction accuracy.

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Ambal salt ridge is a unique exposure of salt piercement in the reservoir of Gotvand dam in the southwest of Iran. It is composed of evaporitic Gachsaran Formation of Oilgo-Miocene Age. This structurally controlled piercement is accompanied by subsidence and sliding of highly soluble layers into the dam reservoir. The region is affected by neotectonic activity due to proximity to two known active faults namely, Lahbari and Pir-Ahmad thrust faults. Based on a four year field observation and monitoring, a gradual and continuous sliding is occurring that is intensified by ground water circulation through evaporite karstic sinkholes and fracture systems. The subsidence and sliding of the Gachsaran evaporitic layers increased significantly after a severe flash flooding in March 2019. The water level rising due to flood event caused filling and saturation of the existing sinkholes in the salt ridge that facilitated and prompted development of land sliding. The situation is expected to be more critical if a moderate to high earthquake would happen since the dam lies in an active tectonic zone of the Zagros Fold Belt. Land subsidence and sliding was facilitated by high fracturing due to neotectonic activity. Finally, based on the Newmark method, slide potential of the largest landslide body of the Ambal ridge was calculated considering geotechnical parameters obtained from core drilling and partial saturation of the salt body during March, 2019 flooding of Karun River.