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CONTENTS
Volume 32, Number 6, December 2023
 

Abstract
In this article, thermal post-buckling and primary resonance of the porous functionally graded material (FGM) beams in thermal environment considering the geometric imperfection are studied, the material properties of FGM beams are assumed to vary along the thickness of the beam, meanwhile, the porosity volume fraction, geometric imperfection, temperature, and the elastic foundation are considered, using the Euler-Lagrange equation, the nonlinear vibration equations are derived, after the dimensionless processing, the dimensionless equations of motion can be obtained. Then, the two-step perturbation method is applied to solve the vibration problems, the resonance and thermal post-buckling response relations are obtained. Finally, the functionally graded index, the porosity volume fraction, temperature, geometric imperfection, and the elastic foundation on the resonance behaviors of the FGM beams are presented. It can be found that these parameters can influence the thermal postbuckling and primary resonance problems.

Key Words
elastic foundation; functionally graded beams; porosity; resonance; thermal post-buckling

Address
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China

Abstract
This work is the first to apply nonlocal theory and a variety of deformation plate theories to study the issue of forced vibration and buckling in organic nanoplates, where the effect of the drag parameter inside the structure has been taken into consideration. Whereas previous research on nanostructures has treated the nonlocal parameter as a fixed value, this study accounts for its effect, and finds that its value fluctuates with the thickness of each layer. This is also a new point that no works have mentioned for organic plates. On the foundation of the notion of potential movement, the equilibrium equation is derived, the buckling issue is handled using Navier's solution, and the forced oscillation problem is solved using the finite element approach. Additionally, a set of numerical examples exhibiting the forced vibration and buckling response of organic nanoplates are shown. These findings indicate that the nonlocal parameter and the drag parameter of the structure have a substantial effect on the mechanical responses of organic nanoplates.

Key Words
buckling; FEM; forced vibration; nanoplates; Navier solution; Newmark; nonlocal theory

Address
Dao Minh Tien: 1) Air Force-Air Defence Technical Institute, Hanoi City, Vietnam, 2) Graduate University of Science and Technology, VietNam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Hanoi City, Vietnam
Do Van Thom and Phung Van Minh: Faculty of Mechanical Engineering, Le Quy Don Technical University, Hanoi City, Vietnam
Nguyen Thi Hai Van: Faculty of Industrial Education, The University of Danang—University of Technology and Education, 48 Cao Thang, Danang, Vietnam
Abdelouahed Tounsi: 1) Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Eastern Province, Saudi Arabia, 2) Material and Hydrology Laboratory, University of Sidi Bel Abbes, Faculty of Technology, Civil Engineering Department, Algeria, 3) YFL (Yonsei Frontier Lab), Yonsei University, Seoul, Korea
Dao Nhu Mai: Institute of Mechanics, Vietnam Academy of Science and Technology, Hanoi, Vietnam

Abstract
In this study, a 3D microstructure-based model is established to simulate the effective thermal conductivity of cement paste, covering varying influencing factors associated with microstructure and thermal transfer mechanisms. The virtual cement paste divided into colloidal C-S-H and heterogeneous paste are reconstructed based on its structural attributes. Using the twolevel hierarchical cement pastes as inputs, a lattice Boltzmann model for heat conduction is presented to predict the thermal conductivity. The results suggest that due to the Knudsen effect induced by the nanoscale pore, the thermal conductivity of air in C-S-H gel pore is significantly decreased, maximumly accounting for 3.3% thermal conductivity of air at the macroscale. In the cement paste, the thermal conductivities of dried and saturated cement pastes are stable at the curing age larger than 100 h. The high water-to-cement ratio can decrease the thermal conductivity of cement paste.

Key Words
hierarchical structure; Knudsen effect; Lattice Boltzmann method; microstructure-based model; thermal conductivity

Address
Cheng Liu, Qiang Liu and Jianming Gao: Jiangsu Key Laboratory for Construction Materials, School of Materials Science and Engineering, Southeast University, Nanjing 211189, China
Yunsheng Zhang: 1) Jiangsu Key Laboratory for Construction Materials, School of Materials Science and Engineering, Southeast University, Nanjing 211189, China, 2) Key Laboratory of Disaster Prevention and Mitigation in Civil Engineering of Gansu Province, Lanzhou University of Technology, Lanzhou 730050, China

Abstract
Engineered cementitious composites with calcined clay limestone cement (LC3-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC3-ECC compressive strength makes the prediction of LC3-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC3-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC3-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC3-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R2) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC3-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

Key Words
engineered cementitious composites (ECC); green concrete; limestone calcined clay cement (LC3); metaheuristic optimization; support vector regression

Address
Enming Li: Universidad Politécnica de Madrid-ETSI Minasy Energía, Ríos Rosas 21, Madrid 28003, Spain
Ning Zhang: Leibniz Institute of Ecological Urban and Regional Development (IOER), Weberplatz 1, 01217 Dresden, Germany
Bin Xi: Department of Civil and Environmental Engineering, Politecnico Di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy
Vivian WY Tam: School of Engineering, Design and Built Environment, Western Sydney University, Sydney, Australia
Jiajia Wang: Department of Real Estate and Construction, The University of Hong Kong, Hong Kong SAR, China
Jian Zhou: School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Abstract
Dune sand (DS) has been widely used as a partial replacement for regular sand in concrete construction. Therefore, investigating its mechanical properties is critical for the analysis and design of structural elements using DS as a construction material. This paper presents a comprehensive investigation of the mechanical properties of DS concrete, considering different replacement ratios and strength grades. Regression analysis is utilized to develop strength prediction models for different mechanical properties of DS concrete. The proposed models exhibit high calculation accuracy, with R2 values of 0.996, 0.991, 0.982, and 0.989 for cube compressive strength, axial compressive strength, splitting tensile strength, and elastic modulus, respectively, and an error within +-20%. Furthermore, a stress-strain relationship specific to DS concrete is established, showing good agreement with experimental results. Additionally, nonlinear finite element analysis is performed on concrete-filled steel tube columns incorporating DS concrete, utilizing the established stress-strain relationship. The analytical and experimental results exhibit good agreement, confirming the validity of the proposed stress-strain relationship for DS concrete. Therefore, the findings presented in this paper provide valuable references for the design and analysis of structures utilizing DS concrete as a construction material.

Key Words
dune sand concrete; finite element analysis; mechanical properties; stress-strain relationship

Address
Said Ikram Sadat, Fei Lyu and Naqi Lessani: School of Civil Engineering, Central South University, Changsha 410075, China
Fa-xing Ding: 1) School of Civil Engineering, Central South University, Changsha 410075, China, 2) Engineering Technology Research Center for Prefabricated Construction Industrialization of Hunan Province, Changsha 410075, China
Xiaoyu Liu and Jian Yang: The 1st Engineering Co., Ltd of China Railway Urban Construction, Changsha, 410023, China

Abstract
The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

Key Words
compressive strength; GBM; geopolymer concrete; GLM; machine learning; XRT

Address
Department of Civil Engineering, Jamia Millia Islamia, New Delhi, India

Abstract
Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

Key Words
attention mechanism; crack detection; dam concrete structures; deep learning; focal loss; U-Net

Address
Zongjie Lv and Yangtao Li: 1) The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210024, China, 2) College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210024, China
Jinzhang Tian: 1) National Dam Safety Research Center, Wuhan, Hubei 430010, China, 2) Changjiang Survey, Planning, Design and Research Co.,Ltd., Wuhan 430010,China
Yantao Zhu: 1) The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210024, China, 2) College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210024, China, 3) National Dam Safety Research Center, Wuhan, Hubei 430010, China


Abstract
The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

Key Words
fire exposure, LGBM; machine learning, reinforced-concrete(RC), XGB

Address
HyunKyoung Kim and Hyo-Gyoung Kwak: Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
Ju-Young Hwang: Department of Civil Engineering, Dong-Eui University, 176 Eomgwangno, Busanjin-gu, Busan, Republic of Korea

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