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Investigating the performance of geogrid reinforced unbound layer using light weight deflectometer (LWD)
, Article International Journal of Pavement Research and Technology ; Volume 15, Issue 1 , 2022 , Pages 173-183 ; 19966814 (ISSN) ; Khabiri, S ; Asgharzadeh, S. M ; Abdollahi, S. F ; Sharif University of Technology
Springer
2022
Abstract
This study investigates the performance of geogrid-reinforced unbound pavement layer. In this study, geogrid was used in various layouts and numbers using three different gradation of granular materials. Light weight deflectometer (LWD) device is known as a useful tool to evaluate the stiffness of unbound pavement layers. In this study, the LWD was utilized to experimentally investigate factors affecting the performance of geogrid reinforcement, including the number of geogrids, geogrid layout, and the gradation of unbound layer. There are several parameters which affect the LWD test results including the hammer weight, the falling height, and the surface stress. The effect of these...
Subcutaneous insulin administration by deep reinforcement learning for blood glucose level control of type-2 diabetic patients
, Article Computers in Biology and Medicine ; Volume 148 , 2022 ; 00104825 (ISSN) ; Niazmand, V. R ; Eqra, N ; Vatankhah, R ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Background: Type-2 diabetes mellitus is characterized by insulin resistance and impaired insulin secretion in the human body. Many endeavors have been made in terms of controlling and reducing blood glucose via the medium of automated controlling tools to increase precision and efficiency and reduce human error. Recently, reinforcement learning algorithms are proved to be powerful in the field of intelligent control, which was the motivation for the current study. Methods: For the first time, a reinforcement algorithm called normalized advantage function (NAF) algorithm has been applied as a model-free reinforcement learning method to regulate the blood glucose level of type-2 diabetic...
Prediction of the degree of steel corrosion damage in reinforced concrete using field-based data by multi-gene genetic programming approach
, Article Soft Computing ; Volume 26, Issue 18 , 2022 , Pages 9481-9496 ; 14327643 (ISSN) ; Eftekhari, M ; Ghorbani, M ; Ehteshamzadeh, M ; Beirami, H ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2022
Abstract
Unanticipated failure of reinforced concrete structures due to corrosion of steel rebar embedded in concrete causes to increase the demand for finding methods to forecast the service life of concrete structures. In this field, the success of machine learning-based methods leads to the use of multi-gene genetic programming (MGGP) method for classifying the degree of corrosion destruction of steel in reinforced concrete in this paper. Despite the common application of MGGP that is the symbolic regression, in this research, MGGP was adapted to use in classification tasks. Accordingly, a large field database has been collected from different regions in the Persian Gulf for modeling of MGGP and...
Active learning of causal structures with deep reinforcement learning
, Article Neural Networks ; Volume 154 , 2022 , Pages 22-30 ; 08936080 (ISSN) ; Salehkaleybar, S ; Hashemi, M ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing...
Development of consistent fish-bone simplified model with energy-based approach for efficient seismic evaluation of irregular steel moment resisting frames
, Article Soil Dynamics and Earthquake Engineering ; Volume 161 , 2022 ; 02677261 (ISSN) ; Ahmadie Amiri, H ; Esmailpur Estekanchi, H ; Kheirkhah Gildeh, M ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
This study aims to develop the Consistent Fish-Bone (CFB) simplified model for efficient estimation of Engineering Demand Parameters (EDPs) in irregular steel Moment Resisting Frames (steel-MRFs). To achieve this goal, some modifications based on the energy consistent approach have been applied to the Improved Fish-Bone (IFB) simplified model previously presented for reinforced concrete MRFs. These modifications include: 1) adding truss elements to the IFB model to consider the effect of flexural deformations and determining their areas by balancing the overturning moment and the strain energy due to the axial deformation of columns in the original steel-MRF with the overturning moment and...
Assessment of fiber-reinforcement and foam-filling in the directional energy absorption performance of a 3D printed accordion cellular structure
, Article Composite Structures ; Volume 297 , 2022 ; 02638223 (ISSN) ; Mahdi Ashrafian, M ; Behravesh, A. H ; Kaveh Hedayati, S ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
This paper presents an evaluation of two distinct techniques to improve energy absorption capability of an accordion cellular structure with close-to-zero Poisson's ratio. For this purpose, fiber-reinforcement and foam-filling methods were employed to address the material enhancement and to obtain light and tough structures with high specific energy absorption. Employing an innovated additive manufacturing based on material extrusion process, specimens of glass fiber-reinforced PLA were produced in both in-plane directions to compare with the un-reinforced counterpart. In continue, some of the 3D printed samples were strengthened by means of polyurethane foam in their hollow structure. The...
Machine learning-based seismic damage assessment of non-ductile RC beam-column joints using visual damage indices of surface crack patterns
, Article Structures ; Volume 45 , 2022 , Pages 2038-2050 ; 23520124 (ISSN) ; Mansourdehghan, S ; Asjodi, A. H ; Dolatshahi, K. M ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
After a significant earthquake, the updated status of the structural elements is usually determined based on a qualitative visual inspection. Although visual inspection provides a prompt assessment of the damaged elements, the output of this subjective method is influenced by the experience and decision of a trained inspector, which may vary from case to case. In this study, an innovative machine learning-based procedure is developed to automate damage state identification of non-ductile reinforced concrete moment frames (RCMFs) utilizing visual indices of crack patterns of the concrete surface. An extensive database including 264 surface crack patterns is constructed corresponding to 61...
Traffic flow control using multi-agent reinforcement learning
, Article Journal of Network and Computer Applications ; Volume 207 , 2022 ; 10848045 (ISSN) ; Javadpour, A ; Bolouki, S ; Sangaiah, A. K ; Ja'fari, F ; Pinto, P ; Zhang, W ; Sharif University of Technology
Academic Press
2022
Abstract
One of the technologies based on information technology used today is the VANET network used for inter-road communication. Today, many developed countries use this technology to optimize travel times, queue lengths, number of vehicle stops, and overall traffic network efficiency. In this research, we investigate the critical and necessary factors to increase the quality of VANET networks. This paper focuses on increasing the quality of service using multi-agent learning methods. The innovation of this study is using artificial intelligence to improve the network's quality of service, which uses a mechanism and algorithm to find the optimal behavior of agents in the VANET. The result...
Elastic responses of bi-material media reinforced by interfacial thin films under asymmetric loading
, Article International Journal of Solids and Structures ; Volume 254-255 , 2022 ; 00207683 (ISSN) ; Jarfi, H ; Eskandari, M ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
In this paper, the elastic solution to the problem of an isotropic bi-material full-space reinforced by a perfectly bonded thin film across the media interface and subjected to arbitrary asymmetric interfacial loading is addressed. To not include any simplifications, the thin film is first considered as a layer having a finite thickness, and with the aid of Fourier expansion of Muki's potential functions in Hankel transformed space, the formulation of the problem is obtained for a tri-material full-space. Then, knowing that the flexural stiffness of the thin film is negligible, its thickness and shear modulus are assumed to tend to zero and infinity, respectively, such that its in-plane...
Developing a novel technique for the fabrication of PLA-graphite composite filaments using FDM 3D printing process
, Article Ceramics International ; Volume 48, Issue 21 , 2022 , Pages 31850-31858 ; 02728842 (ISSN) ; Sayedain, S. S ; Tavangarifard, M ; Alizadeh, R ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Developing a uniform polymer-based composite filament is a critical factor for a successful 3D printing process. In this regard, a novel technique for the fabrication of PLA-graphite filament with the potential to be applied to other PLA-based composite filaments was proposed and compared to the solvent casting method. This modified mixing technique involves partial dissolution of the PLA pellets surface by dichloromethane (DCM), which creates a sticky surface for the strong adhesion of reinforcement powders. The manufactured composite filament by this method exhibited excellent structural features, while the solvent casting method yielded a heterogeneous filament with a non-uniform diameter...
A constrained multi-item EOQ inventory model for reusable items: Reinforcement learning-based differential evolution and particle swarm optimization
, Article Expert Systems with Applications ; Volume 207 , 2022 ; 09574174 (ISSN) ; Amani Bani, E ; Akhavan Niaki, S. T ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
The growing environmental concerns, governmental regulations, and significant cost savings are the primary motivations for companies to consider the reuse and recovery of products in their inventory system. The previous research ignored several realistic features of reusable items inventory systems, such as the presence of multiple products and operational constraints. For the first time, this paper presents a new multiproduct economic order quantity inventory model for an inventory system of reusable products. The goal of the model is to determine the optimal replenishment quantity and reuse quantity of each item so that the system's total cost is minimized. Several operational constraints...
Robust attitude control of an agile aircraft using improved Q-Learning
, Article Actuators ; Volume 11, Issue 12 , 2022 ; 20760825 (ISSN) ; Emami, S. A ; Banazadeh, A ; Castaldi, P ; Sharif University of Technology
MDPI
2022
Abstract
Attitude control of a novel regional truss-braced wing (TBW) aircraft with low stability characteristics is addressed in this paper using Reinforcement Learning (RL). In recent years, RL has been increasingly employed in challenging applications, particularly, autonomous flight control. However, a significant predicament confronting discrete RL algorithms is the dimension limitation of the state-action table and difficulties in defining the elements of the RL environment. To address these issues, in this paper, a detailed mathematical model of the mentioned aircraft is first developed to shape an RL environment. Subsequently, Q-learning, the most prevalent discrete RL algorithm, will be...
Machine learning-aided scenario-based seismic drift measurement for RC moment frames using visual features of surface damage
, Article Measurement: Journal of the International Measurement Confederation ; Volume 205 , 2022 ; 02632241 (ISSN) ; Mansourdehghan, S ; Asjodi, A. H ; Dolatshahi, K.M ; Sharif University of Technology
Elsevier B.V
2022
Abstract
This paper presents a novel computer vision-based methodology for assessment of the seismic damage in reinforced concrete moment frames using visual characteristics of surface damage following an earthquake. An extensive collected database comprising 974 images associated with 256 cyclic-loaded damaged beam-column joints, providing a set of cracking and crushing progression with increasing the evolution of damage level, is collected and used for the development and validation of the methodology. Employing image processing techniques, the characteristics of the surface damage, including the cracking length and crushing areas, are measured and used in a scenario-based assessment for the...
Data-driven damage assessment of reinforced concrete shear walls using visual features of damage
, Article Journal of Building Engineering ; Volume 53 , 2022 ; 23527102 (ISSN) ; Dolatshahi, K. M ; Asjodi, A. H ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
This paper proposes a damage assessment framework based on the visual features of a damaged reinforced concrete shear wall, such as crack pattern distribution, crushing areal density, aspect ratio, and the presence of the boundary condition. The study contains two parts including: identifying the performance level of the damaged walls (i.e., Immediate Occupancy, Life Safety, and Collapse Prevention) and estimating the residual strength and drift ratio of the walls. The research database contains 236 images of 72 reinforced concrete shear walls tested in the laboratory under the quasi-static cyclic loadings at various drift ratios between 0 and 4%. To identify the performance level of a...
PO43--Loaded zif-8-type metal-organic framework-decorated multiwalled carbon nanotube synthesis and application in silane coatings for achieving a smart corrosion protection performance
, Article Industrial and Engineering Chemistry Research ; Volume 61, Issue 32 , 2022 , Pages 11747-11765 ; 08885885 (ISSN) ; Ramezanzadeh, M ; Fedel, M ; Ramezanzadeh, B ; Mahdavian, M ; Sharif University of Technology
American Chemical Society
2022
Abstract
In this research study, interfacial assembled nanoporous zeolitic imidazolate framework (ZIF-8) multiwalled oxidized carbon nanotubes (MW-OCNTs) were developed and introduced into a silane coating. To analyze the destructive behavior of the nanoparticles exposed to salty solutions with three distinctive pHs of 2, 7.5, and 12, various types of tests such as X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and inductively coupled plasma (ICP) were accomplished. The XRD test revealed that the main characteristic peaks of ZIF-8 were eliminated and/or their intensity decreased. In accordance with the obtained data from the XRD test, nanoparticles at pH = 2 had been...
MgO-incorporated carbon nanotubes-reinforced Mg-based composites to improve mechanical, corrosion, and biological properties targeting biomedical applications
, Article Journal of Materials Research and Technology ; Volume 20 , 2022 , Pages 976-990 ; 22387854 (ISSN) ; Shamsipur, A ; Bakhsheshi-Rad, H. R ; Keshavarz, M ; Kehtari, M ; Ramakrishna, S ; Berto, F ; Sharif University of Technology
Elsevier Editora Ltda
2022
Abstract
In this study, magnesium oxide (MgO) nanoparticles are incorporated on carbon nanotubes (CNTs) to reinforce Mg-3Zn-1Mn alloy (ZM31 alloy) by semi-powder metallurgy, followed by hot extrusion, with the purpose of improving the mechanical and biological properties of Mg-based alloy. The microstructural analysis of the nanocomposites indicated a reduction in grain size of Mg alloy with the incorporation of CNTs with a maximum reduction of 61% (ZM31/CNTs), with further reduction in grain size (68%) detected when MgO integrated CNTs composites (ZM31/MgO-CNTs). The compression characteristics of the composites indicate an increase in ultimate compressive strength of 36% and 44%, respectively, with...
Design and characterization of an orthotropic accordion cellular honeycomb as one-dimensional morphing structures with enhanced properties
, Article Journal of Sandwich Structures and Materials ; Volume 24, Issue 3 , 2022 , Pages 1726-1745 ; 10996362 (ISSN) ; Ashrafian, M. M ; Fotouhi, M ; Sharif University of Technology
SAGE Publications Ltd
2022
Abstract
This study develops the governing equations and characterizes the mechanical properties of a new orthotropic accordion morphing honeycomb structure containing periodic arrays of U-type beams reinforced with glass fibers. Castigliano’s second theorem is modified to develop the analytical equations to predict the deformation behavior of a single orthotropic ply under a combined axial, bending, and shear loadings. Accordingly, the elastic properties of the orthotropic structure including elastic stiffness, shear stiffness, and in-plane Poisson’s ratios are calculated by the developed equations. The honeycomb structure is manufactured by 3D printing, and the samples are subjected to tensile...
Model-free LQR design by Q-function learning
, Article Automatica ; Volume 137 , 2022 ; 00051098 (ISSN) ; Babazadeh, M ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Reinforcement learning methods such as Q-learning have shown promising results in the model-free design of linear quadratic regulator (LQR) controllers for linear time-invariant (LTI) systems. However, challenges such as sample-efficiency, sensitivity to hyper-parameters, and compatibility with classical control paradigms limit the integration of such algorithms in critical control applications. This paper aims to take some steps towards bridging the well-known classical control requirements and learning algorithms by using optimization frameworks and properties of conic constraints. Accordingly, a new off-policy model-free approach is proposed for learning the Q-function and designing the...
Peak drift ratio estimation for RC moment frames using multifractal dimensions of surface crack patterns
, Article Engineering Structures ; Volume 255 , 2022 ; 01410296 (ISSN) ; Ganjizadeh, A ; Dolatshahi, K. M ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
In this paper, a novel computer-vision based methodology is developed for predicting the seismic peak drift ratio of damaged reinforced concrete moment frames using surface crack patterns. A comprehensive database comprising 974 surface crack images from cyclic test results of 256 beam-column joint specimens at various drift ratio levels is collected. The database covers a broad range of concrete compressive strengths, rebar and stirrup strengths, longitudinal and transverse reinforcement ratios, beam and column length to depth ratios, in-plane configurations, and failure modes. Multifractal dimensions of damaged beam-column subassembly images are obtained by the box-counting algorithm to...
Investigation on dynamic stability and aeroelastic characteristics of composite curved pipes with any yawed angle
, Article Composite Structures ; Volume 284 , 2022 ; 02638223 (ISSN) ; Chen, J ; Duan, R ; Habibi, M ; Khadimallah, M. A ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
For the first time, in the current work, a dynamic stability analysis of a tilted curved pipe in a supersonic airflow under thermal loading is presented. The heat-transfer continuum problem is used for simulating the thermal environment conditions. The tilted pipe is reinforced by carbon nanotube agglomerations (CNTAs). For simulating the displacement fields of the current structure, Quasi-2D refined high order shear deformation theory is studied. The verification segment is divided into two parts. In the first and second sections, the credibility of the results of this study are confirmed by the results extracted using COMSOL multiphysics software and published articles in the literature,...