Workability and mechanical behavior of steel fiber reinforced self-compacting concrete with supplementary cementitious materials

Memon, Mahmood; Memon, Noor Ahmed; Memon, Bashir Ahmed; Memon, Aftab Hameed; Memon, Abdul Qadir

doi:10.1080/09276440.2022.2164424

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…In this study, we use the kinematic and dynamic model of a mobile robot when there is a side slip as the control object so that this WMR follows a given trajectory and can compensate for the side slip using DSC, AFDSC, and AFNNDSC. The DSC technique is developed based on a multistage sliding controller and backstepping techniques [14,15,19]. Not only does it retain the advantages of dealing with uncertain components in the system model, but DSC also overcomes the disadvantages of these two methods by integrating a low-pass filter into the controller [41].…”

Section: Dynamics Of Non-holonomic Wheeled Mobile Robots (Wmr)mentioning

confidence: 99%

“…Choose a circular orbit with the following equation of motion: x = 5 cos( π 15 t); y = 5 sin( π 15 t); θ = π 15 t + π 2 . DSC control parameter set: C 1 = diag (11,11,11); (15,15,15), λ = diag(10, 10, 10). Tracking trajectory and tracking error when using DSC with circular orbit is shown Figures 4 and 5.…”

Section: A In Case There Is No Interferencementioning

confidence: 99%

“…Thanks to the advancement of theory and control techniques, there have been many different control methods applied to design control laws for mobile robots, such as sliding control [7,8], robust control [9], adaptive control [10][11][12], backstepping control [13,14], and output feedback linearization [15]. These control laws were designed with the assumption that "the wheel only rolls without slipping".…”

Section: Introductionmentioning

confidence: 99%

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Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheeled Slip for Mobile Robot

Hà,

Thuong,

Thanh

et al. 2024

Actuators

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In this article, the research team systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These control algorithms were developed based on dynamic surface control (DSC). An adaptive trajectory tracking DSC algorithm using a fuzzy logic system (AFDSC) and a radial neural network (RBFNN) with a fuzzy logic system were used to overcome the disadvantages of DSC and expand the application domain for non-holonomic wheeled mobile robots with lateral slip (WMR). However, this adaptive fuzzy neural network dynamic surface control (AFNNDSC) adaptive controller ensures the closed system is stable, follows the preset trajectory in the presence of wheel slippage model uncertainty, and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on the Matlab/simulink software R2022b and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the theoretical analysis’ correctness, the proposed controller’s effectiveness, and the possibility of practical applications. Orbits are set as two periodic functions of period T as follows.

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Section: Dynamics Of Non-holonomic Wheeled Mobile Robots (Wmr)mentioning

confidence: 99%

Section: A In Case There Is No Interferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheeled Slip for Mobile Robot

Hà,

Thuong,

Thanh

et al. 2024

Actuators

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“…Since 1874, Bernard (Yu et al, 2022) pioneered the exploration of SFRC by attempting to enhance the tensile strength of plain concrete using steel splinters. Subsequent to this early effort, numerous scholars have extensively investigated various facets of SFRC, covering aspects such as workability (Memon et al, 2023; Yogesh et al, 2023), mechanical properties (Song and Hwang, 2004; Zhao et al, 2023), durability (Su et al, 2023; Zhang et al, 2019), fatigue resistance (da Silva et al, 2022; Koorikkattil et al, 2023) and disaster resistance (Nematzadeh et al, 2021). Concurrently, researchers have proposed innovative shape designs for steel fibers to reinforce materials (Rezakhani et al, 2021; Wu et al, 2016, 2019) and suggested the use of a magnetic field to align fibers during casting (Hajforoush et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Mesoscale modeling of flexural fracture behavior in steel fiber reinforced concrete

Yu,

Xu,

Chen

et al. 2024

Advances in Structural Engineering

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This paper presents a computational study on the flexural fracture behaviors of steel fiber reinforced concrete (SFRC). The focus is on investigating the impacts of various factors on SFRC, utilizing a discrete-continuum coupled finite element method. This method explicitly models each material phase, including coarse aggregates, mortar paste, steel fibers and interfacial transition zones (ITZs), allowing precise tracking of mesoscale cracking during bending. The simulation method is developed, calibrated and validated before conducting a parametric investigation. Critical factors considered include the spatial positioning of coarse aggregates and steel fibers, fiber content, length and diameter, and the bonding property of fiber-mortar ITZs. Results indicate that steel fibers modify crack development in notched beams, causing greater distortion in the primary crack. Increasing fiber content from 0 to 2% enhances flexural tensile strength but introduces more variability. Longer fibers initially increase strength, then decrease, while thicker fibers consistently reduce strength. Improving the bond between fibers and mortar does not substantially increase the load-bearing capacity of the beam. In conclusion, this study shows how the established approach enhances understanding of the mechanical responses of SFRC under flexural-fracture loading.

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“…The technology works at a frame rate of 30 frames per second (FPS). [5] In the paper, a three-wheel Robot prototype has been created to communicate with Google Maps API on a modest scale, taking into account the various characteristics and costs, reaching the destination, and identifying impediments to travel steadily automatically. The physical module of the robot is equipped with several sensors that allow it to communicate with the API.…”

mentioning

confidence: 99%

Self-Driving Car Using Supervised Learning

Collins

Maurya

Ragul

2023

IoT, Cloud and Data Science

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In the growing field of autonomous driving, minimizing human errors lead to fatal accidents. Many companies and research groups have been working for years to achieve a fully autonomous vehicle. Self-driving cars are inevitably the future. The system to be implemented is to train and directly translate images from three cameras to steering commands. The proposed method is expected to work on local roads with or without lane markings. With just the images generated and the human steering angle as the training signal, the proposed method is expected to automatically recognize important road characteristics. A simulator (Udacity) is used to generate data for the proposed model.

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Workability and mechanical behavior of steel fiber reinforced self-compacting concrete with supplementary cementitious materials

Cited by 3 publications

References 19 publications

Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheeled Slip for Mobile Robot

Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheeled Slip for Mobile Robot

Mesoscale modeling of flexural fracture behavior in steel fiber reinforced concrete

Self-Driving Car Using Supervised Learning

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