A Simplified Method for Predicting Bond-Slip Behaviour of Ribbed Bars and Threaded Rods Glued in Glulam along the Grain.

The bond between a steel reinforcement/rod and glulam plays a crucial role in the resistance and deformation capacity of timbers joints. Existing studies provide different bond-slip models for reinforcements and rods with different anchorage lengths, in which the relationship between local bond stress and global bond behaviour cannot not be established. This study presents a unified analytical method for predicting the bond-slip behaviour of ribbed bars and threaded rods along the grain using a local bond-slip model of reinforcement at the elastic and post-yield stages. In the analytical method, equilibrium, compatibility, and constitutive models for reinforcement and rods are considered. The method is verified using test data of rebars and rods with different anchorage lengths. Comparisons between the experimental and calculated results suggest that the analytical method yields reasonably good predictions of the load-slip relationship and failure mode. Furthermore, the embedment lengths required for yield and the ultimate strengths of the reinforcement and rods along the grain are determined by assuming uniform bond stress distributions over the elastic and post-yield steel segment. The average bond stress over the entire anchorage length is calculated and compared with existing equations. Design recommendations for anchorage lengths are proposed for ribbed bars and threaded rods glued in glulam.

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design.

Concrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete mixtures. The concrete mix design methods currently used in engineering practice are joint analytical and laboratory procedures derived from the Three Equation Method and do not perform well enough for the needs of modern concrete technology. This often causes difficulties in predicting the final properties of the designed mix and leads to precautionary oversizing of concrete properties for fear of not providing the required parameters. A new approach that would make it possible to predict the newly designed concrete mix properties is highly desirable. The answer to this challenge can be methods based on machine learning, which have been intensively developed in recent years, especially in predicting concrete compressive strength. Machine learning-based methods have been more or less successful in predicting concrete compressive strength, but they do not reflect well the variability that characterises the currently used concrete mixes. A new adaptive solution that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete is proposed herein. In presented study, a machine learning model was built with a deep neural network architecture, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were calculated according to contemporary design methods (Bolomey and Fuller method), and a comparative analysis was conducted. It was found out that the new algorithm performs significantly better than that without adaptive features trained on the same dataset. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process.

Robust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO.

A robust adaptive recurrent cerebellar model articulation controller (RARC) neural network for non-linear systems using the genetic particle swarm optimization (GPSO) algorithm is presented in this study. The RARC is used as the principal tracking controller and the robust compensation controller is designed to recover the residual of the approximation error. In the RARC neural network, the steepest descent gradient method and the Lyapunov function are used for deriving the adaptive law parameter of the system. Besides, the learning rates play an important role in these adaptive laws and they have a great effect on the functions of control systems. In this paper, the combination of the genetic algorithm with the mutation particle swarm optimization algorithm is applied to seek for the optimal learning rates of the RARC adaptation laws. The numerical simulations about the inverted pendulum system as well as the robot manipulator system are given to confirm the effectiveness and practicability of the GPSO-RARC-based control system. Compared with other control schemes, the proposed control scheme is testified to be reliable and can obtain the optimal parameter about the learning rates and the minimum root mean square error for non-linear systems.