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Volume 30, Number 2, August 2022

An analytical approach for the free vibration behavior of a sandwich cylindrical shell with a functionally graded (FG) core is presented. It is considered that the FG distribution is in the direction of thickness. The material properties are temperature-dependent. The sandwich cylindrical shell with a FG core is considered with two cases. In the first model, i.e., Ceramic-FGM-Metal (CFM), the interior layer of the cylindrical shell is rich metal while the exterior layer is rich ceramic and the FG material is located between two layers and for the second model i.e., Metal-FGM-Ceramic (MFC), the material distribution is in reverse order. This study develops Carrera's Unified Formulation (CUF) to analyze sandwich cylindrical shell with an FG core for the first time. Considering the Principle of Virtual Displacements (PVDs) according to the CUF, the dependent boundary conditions and governing equations are obtained. The coupled governing equations are derived using Galerkin's method. In order to validate the present results, comparisons are made with the available solutions in the previous researches. The effects of different geometrical and material parameters on the free vibration behavior of a sandwich cylindrical shell with an FG core are examined.

Key Words
Carrera's unified formulation; free vibration analysis; functionally graded material; principle of virtual displacements; sandwich cylindrical shell

(1) Kamran Foroutan, Habib Ahmadi:
Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran,
(2) Erasmo Carrera:
Mul2 Group, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.

The object of this paper is to investigate the free vibration behavior under the effect of carbon nanotube distribution in functionally graded carbon nanotube-reinforced composite (FG-CNTRC) by using higher-order shear deformation theories. In this work, we present a novel distribution method for carbon nanotubes in the polymer matrix by using a new exponential power law distribution of carbon nanotube volume fraction. It is assumed that the SWCNTs are aligned along the beam axial direction and the distribution of the SWCNTs may vary through the thickness of the beam with different patterns of reinforcement. The rule of mixtures is used in order to obtain material properties of the CNTRC beams. Hamilton's principle is used in deriving the equations of motion. The validity of the free Vibration results is examined by comparing them with those of the known data in the literature. The results that obtained indicate that the carbon nanotube volume fraction distribution play a very important role on the free vibrations characteristics of the CNTRC beam.

Key Words
beam; free vibration; nanotube; nonlinear distribution; shear deformation; volume fraction

(1) Abdelkader Karas:
Fac. Applied Sciences, Synthesis and Catalysis Laboratory LSCT, University of Tiaret, Algeria;
(2) Youcef Tlidji, Abdelkader Karas, Mohamed Zidour:
University of Tiaret, BP 78 Zaaroura, 14000 Tiaret, Algeria;
(3) Rachid Zerrouki, Mohamed Zidour:
Laboratory of Geomatics and Sustainable Development, University of Tiaret, Algeria;
(4) Abdelouahed Tounsi:
YFL (Yonsei Frontier Lab), Yonsei University, Seoul, Korea;
(5) Ahmed Hamidi:
Civil Engineering and Hydraulic Department, Faculty Technology, University of Bechar, Algeria;
(6) Youcef Tlidji:
Materials and Structures Laboratory, University of Tiaret, Faculty of Applied Sciences, Civil Engineering Department, Algeria.

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

Key Words
big data; deep learning; live load survey; web crawler; YOLOv3

(1) Yang Li, Jun Chen:
College of Civil Engineering, Tongji University, Shanghai 200092, China;
(2) Jun Chen:
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China.

To improve the accuracy of the standard penetration test (SPT) results, smart measurement system, which considers the energy transfer ratio into the sampler (ETRSampler), is required. The objective of this study is to evaluate the effects of joints and rod length on the transferred energy into the sampler. The energy transfer ratios into the rod head (ETRHead) and ETRSampler, and the energy ratio from the head to the sampler (ERHS) were obtained using energy modules, which were installed at the rod head and above the SPT sampler. Linear regression analyses are conducted to correlate the ERHS with the number of joints, rod length, and SPT N-values. In addition, the dynamic resistances are calculated using both transferred energies into the rod head and into the sampler, and are compared with the corrected cone tip resistance measured from the cone penetration test (CPT). While the ETRHead are generally constant, but the ETRSampler and ERHS gradually decrease along the depth or the number of joints, except at certain depths with high SPT N-values. Thus, the ERHS can be estimated using the number of joints, rod length, and SPT N-values. The dynamic resistance evaluated by ESampler produces a better correlation with the corrected cone tip resistance than that by EHead. This study suggests that transferred energy into the SPT sampler may be effectively used for more accurate subsurface characterization.

Key Words
dynamic resistance; energy correction; energy module; standard penetration test (SPT); transferred energy

(1) Geunwoo Park, Namsun Kim, Jong-Sub Lee:
School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea;
(2) Won-Taek Hong:
Department of Civil and Environmental Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu , Seongnam-si, Gyeonggi-do, 13120, Republic of Korea.

The current paper presents a technique to detect crack in non-uniform cantilever-type pipe beams, that have step changes in the properties of their cross sections, restrained by a translational and rotational spring with a tip mass at the free end. An equation for estimating the natural frequencies for the non-uniform beams is derived using the boundary and continuity conditions, and an equivalent bending stiffness for cracked beam is applied to calculate the natural frequencies of the crackedbeam. An experimental study for a step-changed non-uniform cantilever-type pipe beam restrained by bolts with a tip mass is carried out to verify the proposed method. The translational and rotational spring constants are updated using the neural network technique to the results of the experiment for intact case in order to establish a baseline model for the subsequent crack detection. Then, several numerical simulations for the specimen are carried out using the derived equation for estimating the natural frequencies of the cracked beam to construct a set of training patterns of a neural network. The crack locations and sizes are identified using the trained neural network for the 5 damage cases. It is found that the crack locations and sizes are reasonably well estimated from a practical point of view. And it is considered that the usefulness of the proposed method for structural health monitoring of the step-changed non-uniform cantilever-type pipe beam-like structures elastically restrained in the ground and have a tip mass at the free end could be verified.

Key Words
baseline model; crack detection; natural frequency; non-uniform; step-changed

Department of Architectural Engineering, Namseoul University, 91 Daehak-ro, Seobuk-gu, Cheonan-si, Chungcheongnam-do 31020, Republic of Korea.

Structural health monitoring and structural vibration control are multidisciplinary and frontier research directions of civil engineering. As intelligent materials that integrate sensing and actuation capabilities, shape memory alloys (SMAs) exhibit multiple excellent characteristics, such as shape memory effect, superelasticity, corrosion resistance, fatigue resistance, and high energy density. Moreover, SMAs possess excellent resistance sensing properties and large deformation ability. Superfine NiTi SMA wires have potential applications in structural health monitoring and micro-drive system. In this study, the mechanical properties and electrical resistance sensing characteristics of superfine NiTi SMA wires were experimentally investigated. The mechanical parameters such as residual strain, hysteretic energy, secant stiffness, and equivalent damping ratio were analyzed at different training strain amplitudes and numbers of loading.unloading cycles. The results demonstrate that the detwinning process shortened with increasing training amplitude, while austenitic mechanical properties were not affected. In addition, superfine SMA wires showed good strain.resistance linear correlation, and the loading rate had little effect on their mechanical properties and electrical resistance sensing characteristics. This study aims to provide an experimental basis for the application of superfine SMA wires in engineering.

Key Words
ER sensing; mechanical properties; shape memory effect; superelasticity; superfine SMA wire

(1) Hui Qian, Boheng Yang, Yonglin Ren:
School of Civil Engineering, Zhengzhou University,100 Science Avenue, Zhengzhou City, the People's Republic of China;
(2) Rende Wang:
Henan Haoze Electronics Co., Ltd., 7 Mengzhou Hi-tech Pioneer Park, Jiaozuo City, the People's Republic of China.

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

Key Words
artificial neural network; concrete compressive strength; hybrid metaheuristic algorithms

(1) Pan Hu:
School of Civil and Architectural Engineering, Technical University of Munich, Munich 80333, Germany;
(2) Zohre Moradi:
Faculty of Engineering and Technology, Department of Electrical Engineering, Imam Khomeini International University, 34149-16818 Qazvin, Iran
(3) Zohre Moradi:
Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai 600 077, India;
(4) H. Elhosiny Ali:
Advanced Functional Materials & Optoelectronic Laboratory (AFMOL), Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia;
(5) H. Elhosiny Ali:
Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha 61413, P.O. Box 9004, Saudi Arabia;
(6) H. Elhosiny Ali:
Physics Department, Faculty of Science, Zagazig University, 44519 Zagazig, Egypt;
(7) Loke Kok Foong:
Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam.

The determination for bearing capacity of precast nodular piles is conventionally time-consuming and high-cost by using numerous experiments and empirical methods. This study proposes an intelligent method to evaluate the bearing capacity and shaft resistance of the nodular piles with high efficiency based on long short-term memory (LSTM) approach. A series of field tests are first designed to measure the axial force, shaft resistance and displacement of the combined nodular piles under different loadings, in comparison with the single pre-stressed high-strength concrete piles. The test results confirm that the combined nodular piles could provide larger ultimate bearing capacity (more than 100%) than the single pre-stressed highstrength concrete piles. Both the LSTM-based method and empirical methods are used to calculate the shift resistance of the combined nodular piles. The results show that the LSTM-based method has a high-precision estimation on shaft resistance, not only for the ultimate load but also for the working load.

Key Words
combined nodular piles; field test; LSTM-based method; shaft resistance

(1) Xiao-Xiao Chen:
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325016, China;
(2) Chang-Sheng Zhan:
Wenzhou Ecological Park Development and Construction Investment Group Co., Ltd, Wenzhou, 325207, China;
(3) Sheng-Liang Lu:
School of Civil Engineering and Architecture, Wenzhou Polytechnic, Wenzhou, 325000, China.

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