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CONTENTS
Volume 28, Number 6, December 2021
 


Abstract
Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.

Key Words
artificial intelligence algorithms; beams without stirrups; design formulae; machine learning; reinforced concrete; shear strength prediction

Address
George Markou: Structures Division, Civil Engineering Department, University of Pretoria, Hatfield Campus, 0028 Pretoria, South Africa
Nikolaos P. Bakas: Research and Development Department, RDC Informatics, 2 Irous St, 10442, Athens, Attica, Greece

Abstract
Nowadays, strengthening of buildings is an inclusive and effective field in civil engineering that is not only applicable to the buildings but also it can be developed for the bridges. Rehabilitation and strengthening of structures are highly recommended for the existing structures due to the alter in codes and provisions as well as buildings' use change. Extensive surveys have been conducted in this field in the world that propose wide variety of methods for strengthening of structures. In recent years, more specific researches have been carried out that present novel materials for rehabilitation beside proposing methods and performing techniques. In the current study, a novel technique for developing the bending capacity of reinforced concrete beams to enhance their performance as well as rehabilitating and reforming the performance of reinforced concrete beams with nonstandard lap splices, has been proposed. In this method, a post-tensioned concrete layer is added to the side face of the concrete beams built in 1:1 scale. Results reveals that addition of the post-tensioned layer enhances the beams' which were subjected to the four-point pushover test after they were reinforced. The testing process ended when the samples reached complete failure status. Results show that the performance and flexural capacity of reinforced beams without lap splice is improved 22.7% compared to the samples without the post-tensioned layer, while it is enhanced up to at least 80% compared to the reinforced beams with nonstandard lap splice. Furthermore, the location of plastic hinges formation was transformed from the beam's mid-span to the 1⁄3 of span's end and the beam's cracking pattern was significantly improved.

Key Words
flexural capacity; lap splice; post-tensioning; reinforced concrete beam; strengthening

Address
Alireza Mirzaee, Ashkan Torabi and Arash Totonchi: Department of Civil Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

Abstract
In this paper, frequency analysis has been done for functionally graded cylindrical shell with ring supports using Sander's shell theory. The vibrations of rotating cylindrical shells are analyzed for different physical factors. The fundamental natural frequency is investigated for different parameters such as: ratios of length-to-diameter ring supports. By increasing different value of height-to-radius ratio, the resulting backward and forward frequencies increase and frequencies decrease on increasing height-to-radius ratio. The frequencies for different position of ring supports are obtained in the form of bell shaped. The backward frequencies increases and forward frequencies decrease on increasing the rotating speed. The results generated furnish the evidence regarding applicability of present shell model and also verified by earlier published literature.

Key Words
frequency response; ring supports; rotating speed; simply supported

Address
Faisal Al Thobiani: Marine Engineering Department, Faculty of Maritime Studie, King Abdulaziz University, Jeddah, Saudi Arabia
ohamed A. Khadimallah: Prince Sattam Bin Abdulaziz University, College of Engineering, Civil Engineering Department, BP 655, Al-Kharj, 11942, Saudi Arabia
Muzamal Hussain: Department of Mathematics, Govt. College University Faisalabad, 38000, Faisalabad, Pakistan
Gar Al-Nabi Ibrahim Mohamed: Hydrographic Surveying Department Faculty of MaritimesStudies, Saudi Arabia
Emad Ghandourah: Department of Nuclear Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

Abstract
The use of waste materials as a binder or aggregate in the concrete mixture is a great step towards sustainability in the construction industry. Waste rubber (WR) can be used as coarse and fine aggregates in concrete and improves the crack resistance, impact resistance, and fatigue life of the produced concrete. However, the mechanical properties of rubberized concrete degrade significantly by replacing the natural aggregate with WR. To have accurate estimations of the mechanical properties of rubberized concrete, two machine learning methods consisting of artificial neural network (ANN) and neuro-fuzzy system (NFS) were served in this study. To do this, a comprehensive dataset was collected from reliable literature, and two scenarios were addressed for the selection of input variables. In the first scenario, the critical ratios of the rubberized concrete and the concrete age were considered as the input variables. In contrast, the mechanical properties of concrete without WR and the percentage of aggregate volume replaced by WR were assumed as the input variables in the second scenario. The results show that the first scenario models outperform the models proposed by the second scenario. Moreover, the developed ANN models are more reliable than the proposed NFS models in most cases.

Key Words
artificial neural network; mechanical properties; neuro-fuzzy system; rubberized concrete; waste rubber

Address
Kaveh Miladirad: Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Emadaldin Mohammadi Golafshani: Department of Civil Engineering, Monash University, VIC 3800, Australia
Majid Safehian: Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Alireza Sarkar: Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract
The validation based on the experimental data demonstrates that the concrete strength under triaxial compression (TC) is overestimated by Lubliner-Oller strength criterion (SC) but underestimated by Lubliner-Lee SC in ABAQUS. Moreover, the discontinuous derivatives of failure criterion exists near the unexpected breakpoints. Both resulted from the piecewise linear meridians of the original Lubliner SC with constants y. Following the screen for the available failure criteria to determine the model parameter y of Lubliner SC, Menétrey-Willam SC (MWSC) is considered the most promising option with a reasonable aspect ratio Kc but no other strength values required and only two new model parameters introduced. The failure surface of the new Lubliner SC based on MWSC (Lubliner-MWSC) is smooth and has no breakpoints along the hydrostatic pressure (HP) axis. Finally, predicted results of Lubliner-MWSC are compared with other concrete failure criteria and experimental data. It turns out that the Lubliner-MWSC can represent the concrete failure behavior, and MWSC is the optimal choice to improve the applicability of the concrete damaged plasticity model (CDPM) under TC in ABAQUS.

Key Words
breakpoints; concrete strength model; linear meridian; optimal failure criteria; triaxial compression

Address
Bo Lei: Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Chengdu 610031, China; School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Taiyue Qi: Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Chengdu 610031, China; School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Rui Wang: School of Engineering, Sichuan Normal University, Chengdu 610101, China
Xiao Liang: Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Chengdu 610031, China; School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China

Abstract
Building Information Modeling (BIM) models are a powerful tool for preserving and using architectural history. Manually creating information models for such a significant number and variety of architectural monuments as Dong drum towers is challenging. The building logic based on "actual measurement construction" was investigated using the metamodel idea, and a metamodel-based automated modeling approach for the wood framework of Dong drum towers was presented utilizing programmable algorithms. Metamodels of fundamental frame kinds were also constructed. Case studies were used to verify the automated modeling's correctness, completeness, and efficiency using metamodel. The results suggest that, compared to manual modeling, automated modeling using metamodel may enhance the model's integrity and correctness by 5-10% while also reducing time efficiency by 10-20%. Metamodel and construction logic offer a novel way to investigate data-driven autonomous information-based modeling.

Key Words
architectural heritage; automatic modeling; BIM models; drum tower; metamodel

Address
Yi Deng: School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, Guangdong, China
Shi HanGuo: School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, Guangdong, China
Ling Cai: Guangdong Provincial Institute of Cultural Relics and Archaeology, Guangzhou 510075, Guangdong, China

Abstract
For the reliable strut-and-tie model (STM) design of disturbed regions of concrete members, structural designers must accurately determine the strength of concrete struts to check the strength conditions of a selected STM el and the anchorage of reinforcing bars in nodal zones. In this study, the author proposed a consistent numerical method for strut strength, applicable to all two-dimensional STMs. The proposed method includes the effects of a biaxial stress state associated with tensile strains in reinforcing bars crossing a strut, deviation angle between strut orientation and compressive principal stress flow, and degree of confinement provided by reinforcement. The author examined the method's validity through the STM prediction of the ultimate strengths of 517 reinforced concrete (RC) deep beams, 24 RC panels, and 258 RC corbels, all tested to failure.

Key Words
concrete strut; disturbed region; strut strength; structural concrete; two-dimensional strut-and-tie model

Address
Y.M. Yun: School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea

Abstract
In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R2 of 0.9292 with an RMSE of 5.35 for training and an R2 of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R2 of 0.9125 with an RMSE of 6.17 for training and an R2 of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

Key Words
artificial neural network; fiber reinforced polymer; particle swarm optimization; prediction; RC beams; torsional strength

Address
Harish Narayana: Department of Civil Engineering, National Institute of Technology Goa, Ponda, Goa, India
Prashanth Janardhan: Department of Civil Engineering, National Institute of Technology Silchar, Assam, India


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