فهرست مطالب a. bakhshi

This research is based on sedimentological study of 4 surface (Kabir kuh, Samand, Anjir and Anaran) and 8 subsurface wells in the Lurestan, North Dezful and Abadan plain with a total thickness of 1592 m. The Santonian – Lower Campanian Ilam Formation is a carbonate hydrocarbon reservoir in the Zagros fold – thrust belt, in SW Iran. Twenty microfacies were recognized based on microscopic study. Microfacies analysis led to the recognition of four facies belts including inner (shoal), mid, outer ramp, and basinal setting. Correlation of depositional environments together with lateral facies changes indicate deposition are distally steepend ramp. Deep marine and inner ramp facies are the most and least facies, respectively in the study area. As the distribution of inner ramp facies is very limited, it can only be recognized in wells from North Dezful and Abadan plain.The results obtained from facies together with analyses of wireline logs clearly shows a northward deepending of the depositional environment from Balarud Fault. Cementation, micritization, compaction, solution seams, neomorphism, disolution, fracturing, pyritization, phosphatization, gluconitization and bioturbation are the main diagentic processes. However, cementation is most dominant in shallow marine facies in the Abadan plain, whereas, pyritization, stylolite and fracture are the most prevailing processes in deeper marine facies of the Ilam Formation in Lurestan. There is a limited distribution of porosity, but the most dominant types are, interpartical, microporosity and fracture related porosity. The Ilam carbonates are mostly affected by shallow to deep burial diagenesis in Lurestan,

In this study, three types of chelated iron containing fertilizers (Sulufeed, Frilan iron cheated with EDDHA, Petrokimia - chelated Fe-EDTA and iron sulfate were compared . This research was conducted in 2013 in two phases. First stage an incubation experiment availability of iron in soil in at two time periods were studied. So in a greenhouse experiment the effect of fertilizer in tree levels (15,30,45 ppm) on plant growth parameters were also investigated. The results of incubation experiment, Frilan fertilizer and sulufied had more soil iron content (6.1 &9.3 mg/kg) at 38 days’ period and at 56 days all treatment, showed a relatively similar levels of iron content. The results of corn growth experiment under fertilizer treatments indicated that highest leaf Fe content (80 mg/kg) and soil Fe content(8mg/kg) after harvest were for the sulufeed  treatment. In general, sulufeed and Frilan had better performance in all experiment with respect to iron uptake on release of iron.

Concrete is one of the major materials used in modern structures. Concrete members are used as the main structural parts of various infrastructures such as dams, tunnels, bridges, and skyscrapers. However, this wide application requires some accurate and efficient inspection system during the structure’s life. Cracks are classified as the earliest symptoms of degradation in concrete members. Although manual inspection is a common method in structural health monitoring and crack detection in civil engineering structures, serious limitations caused by implementing human resources degraded the efficiency of the proposed method. In recent years, many studies tried to automate the inspection of these structures by using different sensors such as Ultrasonic and Piezo-electric sensors, seeming to be costly and insufficient in some cases. With recent development in computer vision techniques, especially deep-learning-based methods, there is an opportunity for researchers to come with autonomous visual inspection systems for structural health monitoring of concrete members. This study proposes a deep-learning-based model for automatic crack detection on the concrete surface. The proposed model is an encoder-decoder model that uses ResNet101, a well-known convolutional neural network, as the encoder and the U-Net’s expansion path as the decoder. To minimize the training time and maximize the accuracy, we use transfer learning in our approach. The dataset implemented for this study includes 458 images of the cracked surface of concrete members which come with corresponding segmentation label masks. Data augmentation techniques strongly increased the robustness of the proposed model encountering different imaging conditions and noises. The proposed model was trained using the backpropagation algorithm and it achieved 99.39% precision and 84.99% recall, which led to a 91.38% F1 score on the unseen test dataset. The accuracy and speed of the present model outperform the existing methods and different crack types composing the dataset help generalize the model for prediction of different crack types and different imaging conditions.

Masonry structures comprise a large proportion of human-made building stocks around the world. In many cases, aged masonry structures have been found to be vulnerable to earthquakes and seismic loads. Due to the historical importance and vulnerable conditions of these structures, an efficient structural health monitoring system is required to detect every sign of degradation. Thus, a suitable restoration scheme could be taken into account. Manual visual inspection is one of the earliest monitoring schemes used to inspect these structures. Due to the limitations and dangers imposed by using human resources, new strategies are required to achieve this purpose. Recent developments in artificial Intelligence and computer vision have helped researchers develop a new generation of autonomous inspection systems. In the present study, we are going to use a deep learning model with an encoder-decoder architecture to automate crack detection in masonry structure images. In the current study, semantic segmentation is proposed as a detailed solution to accurately predict the location and condition of cracks in masonry images. In the development of the main model of the study, we used EfficientNet-B3 as the encoder while the decoder was defined according to U-Net’s expansion path in order to predict the accurate segmentation mask for the corresponding input images. For training and evaluation of the proposed model, a dataset composed of 115 images is generated and manually annotated. In the proposed method, transfer learning is used to train the model and the data augmentation techniques are implemented to achieve the optimal results on the present dataset. Furthermore, using the Dice-Coefficient loss function directly optimizes the model for F1-Score, which is the main evaluation parameter in semantic segmentation tasks. Finally, the evaluation demonstrated 81.444% Precision, 71.411% Recall, and 75.366% F1-Score for the never-seen test data. The study shows that the deep learning approach can be accurate and trustworthy for this task. Also, the limited number of training data and the complex background images in the dataset prove the robustness of the proposed model.

Rapid assessment of structural safety and performance following the occurrence of important events such as moderate to severe earthquakes is so significant and vital and reveals the need for developing online and pseudo-online health monitoring methods. Online monitoring methods can be implemented without the need for in-situ testing and expert staff to analyze the recorded data. In other words, these methods provide comprehensive information on the condition of the structure only by using the vibration data recorded by embedded sensors as well as the preset health monitoring algorithms. On the other hand, most civil structures exhibit a nonlinear response after severe incidents like earthquakes. In many cases, this nonlinear hysteretic behavior is along with stiffness deterioration, strength degradation, pinching effect, permanent plastic deformation, or a combination of them. Therefore, considering a comprehensive definition of damage that takes into account the nonlinear behavior of structures according to their type is one of the most important steps in the process of structural identification and evaluation. Various methods have been introduced in the literature for online estimation of states and parameters of nonlinear structures. However, the challenging part in most of these methods is the determination of parameters noise covariance matrix which becomes particularly important due to the increasing number of structural floors, thus increasing the number of unknown parameters. In this study, an effective method for online jointly estimation of state and parameters of nonlinear hysteretic structures with consideration of degradation and pinching phenomena is proposed. Simultaneous estimation of states and parameters is conducted using a combination of Unscented Kalman Filter as an effective estimator and Robbins-Monroe stochastic approximation technique as the parameters noise covariance matrix regulator. The abovementioned method was applied to two one-story and one three-story shear buildings and the results of the identification process were presented with emphasis on the effects of measurement noise, modeling error, and use of the Monte-Carlo random simulation method. Simulation results demonstrated the accuracy and efficiency of the proposed method in online jointly estimation applications as well as the desirable capability to track hysteretic curves of each structural floor.

Gingival overgrowth is a common clinical finding that most often represents reactive hyperplasia as a result of inflammatory gingival disease related to dental plaque. Irritation fibroma (IF), pyogenic granuloma (PG), peripheral ossifying fibroma (POF), and peripheral giant cell granuloma (PGCG) are non-neoplastic enlargements of the gingiva that are categorized as reactive hyperplastic lesions. Most of these lesions have a similar clinical appearance, i.e., a sessile or pedunculated nodule at the interdental papilla; the color varies from pale pink to red. All reactive gingival lesions show some variations in terms of gender, type, location, duration, and histologic features. Here, we present a case of PGCG in a 10-year-old male with its clinical presentations as well as radiological and histological features along with the management procedure. Choice of treatment for our case was surgical excision, fluoride therapy, and oral hygiene maintenance. The patient remained tumor-free for 18 months and is still on regular follow-up.

This study presents an experimental investigation on mud brick walls with mortar containing straw bers under diagonal axial compression. The brick size was: 22227 cm (facebedend). The average compressive strength of the bricks was 4.73 MPa. To determine the performance of straw bers in the mortar of mud brick wall, 12 walls, with size 12012022 cm, with various percentages of straw bers (0/4, 0/6, 0/8, 1 and 1/2), were investigated. For evaluation of failure modes in ultimate diagonal compressive load, types of crack, load capacity and ductility, and energy absorption characteristics, were studied. Cracks in specimens with mortar reinforced by straw bers have less width than unreinforced mortar. The ultimate load capacities of walls reinforced with various percentages of straw bers (0/4, 0/6, 0/8, 1 and 1/2), were obtained as 20.3, 21.05, 20.7 and 18.5 kN, respectively. The average load capacitiy was increased about 26.78% compared to the unreinforced mortar wall in the ultimate load. The result shows that energy absorption in reinforced mortar specimens increased about 94.22 percent. Ultimate displacement for the unreinforced walls was 23.1 and for reinforced walls with 0/8% straw ber in mortar was 34.8mm. It shows that ultimate displacement increased about 51 percent. Test results show that straw bers can be used for reinforcing the mortar of mud brick walls, which is an advantage both economically and in enhancing structural behavior. In this study, when the straw bers were increased to 0.8 percent in the mortar, the load carrying capacity was increased more than any other percentage of ber in the mortar.

B u r i e d p i p e l i n e n e t w o r k s a r e d e s i g n e d t o t r a n s f e r w a t e r, g a s a n d o i l, a n d c o l l e c t w a s t e w a t e r i n l a r g e i n d u s t r i a l c i t i e s. T h e s i g n i f i c a n c e o f t h e s e p i p e l i n e s a s l i f e l i n e f a c i l i t i e s i s m o r e o b v i o u s i n r e g i o n s w i t h h i g h r i s k o f e a r t h q u a k e d a m a g e. F a i l u r e o f o i l a n d g a s p i p e l i n e s c a n c a u s e s e v e r e e c o n o m i c a l a n d e n v i r o n m e n t a l p r o b l e m s. I n v e s t i g a t i o n s s h o w t h a t d a m a g e t o b u r i e d p i p e l i n e s i s n o t a t t r i b u t e d t o l o a d l e v e l s o f s e i s m o l o g i c a l v i b r a t i o n s. D e s p i t e d a m a g e t o s u r f a c e s t r u c t u r e s, l a r g e f a u l t m o v e m e n t s a r e t h e m a i n c a u s e s o f p i p e l i n e d a m a g e. M a n y r e s e a r c h e r s h a v e s t u d i e d p i p e l i n e b e h a v i o r a g a i n s t t h o s e p h e n o m e n a w i t h s i m p l i f i e d a s s u m p t i o n s, i n w h i c h e f f e c t s o f i n t e r a c t i o n s b e t w e e n s o i l a n d p i p e a r e i g n o r e d. I n t h i s r e s e a r c h, t h e b e h a v i o r o f a p i p e u n d e r n o r m a l a n d s t r i k e-s l i p f a u l t m o v e m e n t s, a n d c o n t a c t b e t w e e n t h e m, i s s i m u l a t e d u s i n g F E M m o d e l i n g t o i n v e s t i g a t e f a u l t m o v e m e n t e f f e c t s o n p i p e l i n e f a i l u r e a n d t o d e s i g n a n a p p r o p r i a t e p i p e l i n e. A l s o, t o r e a c h a n o p t i m i z e d p e r f o r m a n c e, t o d e s i g n p i p e l i n e s a g a i n s t s e i s m i c l o a d a n d t o o f f e r t h e r e l e v a n t e q u a t i o n s, s e v e r a l p a r a m e t e r s, s u c h a s p i p e d i a m e t e r, p i p e w a l l t h i c k n e s s, p i p e b u r i a l d e p t h, c r o s s i n g a n g l e b e t w e e n p i p e a x e s a n d f a u l t, u n a n c h o r e d l e n g t h o f p i p e a n d p i p e s t r e n g t h, a r e c o n s i d e r e d. T h e s e m o d e l s a r e m a d e u p o f t h r e e d i f f e r e n t p a r t s. T w o o f t h e m a r e s o i l b l o c k s, w h i c h a r e d i v i d e d i n t o s i m i l a r p a r t s b y a f a u l t p l a n e. I n o r d e r t o c l a r i f y f a u l t m o v e m e n t s d u r i n g e a r t h q u a k e a n d t h e i r e f f e c t s o n p i p e s, c o n t a c t a n d i n t e r a c t i o n b e t w e e n t h e t w o s i m i l a r s o i l p a r t s a n d a s l i d e b a s e d o n t h e m a x i m u m s h e a r s t r e n g t h b e t w e e n t h e m a r e a p p l i e d. T h e t h i r d p a r t i s a s t e e l p i p e, w h i c h p a s s e s t h r o u g h t h e f a u l t p l a n e i n s i d e t h e s o i l l a y e r s. S o i l a n d p i p e a r e c o n n e c t e d t o e a c h o t h e r b y a p p r o p r i a t e c o n t a c t e l e m e n t s, s o t h a t t h e o u t s i d e s u r f a c e o f t h e p i p e i n t e r a c t s w i t h t h e i n s i d e s u r f a c e o f t h e s o i l w h e r e t h e p i p e i s b u r i e d. I n e a c h p a r t o f t h e m o d e l s, e l a s t o-p l a s t i c m a t e r i a l s a r e u s e d. T h e r e f o r e, i t i s p o s s i b l e t o e v a l u a t e t h e r o l e s o f m e n t i o n e d p a r a m e t e r s b y s e v e r a l a n a l y s e s. T h e r e s u l t s f r o m p a r a m e t r i c a n a l y s i s o n t h e e f f e c t s o f t h o s e f a u l t s o n b u r i e d s t e e l p i p e l i n e s s h o w t h a t i n c r e a s i n g t h e a n g l e o f t h e p i p e a x i s a n d f a u l t l i n e, a s w e l l a s p i p e t h i c k n e s s, a n d d e c r e a s i n g b u r i a l d e p t h d i m i n i s h e s t h e p r o b a b i l i t y o f p i p e f a i l u r e c r o s s i n g a c t i v e f a u l t s. C h a n g e s i n p i p e d i a m e t e r a l s o a f f e c t t h e p i p e s t r e n g t h s l i g h t l y. C o m p a r i s o n b e t w e e n r e s u l t s o f e a r l i e r a n d c u r r e n t r e s e a r c h i n d i c a t e s t h a t t h e y a r e m o r e c o n s e r v a t i v e.