期刊：
International Journal of Sustainable Development,2024年27(1-2):186-200 ISSN：0960-1406
作者机构：
[Yu Liu] Department of Civil Engineering, Jinan Engineering Polytechnic, Jinan, 250200, China;[Lianguang Mo] School of Civil Engineering, Hunan City University, Yiyang, 413000, China
摘要：
Given the problems of large evaluation errors and long time-consuming in the existing methods, a risk evaluation method for construction projects based on grey correlation analysis is designed. First, build the evaluation index system, and divide the index grade to achieve the construction of the index system. Then, a fuzzy matrix is constructed to determine the complementary relationship between different indicators, and the indicators are quantified and verified for consistency. Finally, determine the index time series dataset, eliminate the index difference, and determine the index difference sequence, maximum range and minimum range value. The index correlation coefficient is calculated and sorted, and the improved grey correlation analysis algorithm is introduced to build the evaluation model. The test results show that the proposed method can reduce the evaluation error and has a certain application value.
摘要：
This study explores the potential of recycling and utilizing discarded wind turbine blades. It proposes a new method that upcycles the glass fiber powder (GFP) in these blades into geopolymer gel, offering a new solution for the recycling and treatment of retired wind blades. Analyses of the X-ray diffraction (XRD) pattern of the GFP raw material and its dissolution in concentrated alkali indicate a large content of vitreous and active silica-aluminum, suggesting its suitability for producing geopolymers through alkali activation. The effects of the alkali activator modulus (Ms), alkali-binder ratio (N/B), and water-binder ratio (w/b), raw material particle size, and initial curing temperature on the compressive strength of the specimens were comprehensively and systematically investigated. The reaction behavior, chemical structure, and microstructure of GFP-synthesized geopolymers were characterized by selective dissolution, microcalorimetry, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy equipped with energy dispersive spectroscopy (SEM-EDS). The results showed that gels were formed by the geopolymerization reaction between the GFP and alkali activator. The compressive strength of the specimens increased first and then decreased with increasing alkali activator Ms and N/B. However, increasing w/b led to a decrease in strength. Appropriately small particle sizes of the raw material and moderately increasing the initial curing temperature are beneficial to strengthen the compressive strength. The optimal 28 days compressive strength of 43.43 MPa was achieved with an activator Ms of 1.4, N/B of 10 %, w/b of 0.33, GFP with a D90 of 170 mesh, and an initial 24 h curing temperature of 60 °C. The results of this study can promote the utilization of waste wind blades in the production of sustainable building materials.
This study explores the potential of recycling and utilizing discarded wind turbine blades. It proposes a new method that upcycles the glass fiber powder (GFP) in these blades into geopolymer gel, offering a new solution for the recycling and treatment of retired wind blades. Analyses of the X-ray diffraction (XRD) pattern of the GFP raw material and its dissolution in concentrated alkali indicate a large content of vitreous and active silica-aluminum, suggesting its suitability for producing geopolymers through alkali activation. The effects of the alkali activator modulus (Ms), alkali-binder ratio (N/B), and water-binder ratio (w/b), raw material particle size, and initial curing temperature on the compressive strength of the specimens were comprehensively and systematically investigated. The reaction behavior, chemical structure, and microstructure of GFP-synthesized geopolymers were characterized by selective dissolution, microcalorimetry, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy equipped with energy dispersive spectroscopy (SEM-EDS). The results showed that gels were formed by the geopolymerization reaction between the GFP and alkali activator. The compressive strength of the specimens increased first and then decreased with increasing alkali activator Ms and N/B. However, increasing w/b led to a decrease in strength. Appropriately small particle sizes of the raw material and moderately increasing the initial curing temperature are beneficial to strengthen the compressive strength. The optimal 28 days compressive strength of 43.43 MPa was achieved with an activator Ms of 1.4, N/B of 10 %, w/b of 0.33, GFP with a D90 of 170 mesh, and an initial 24 h curing temperature of 60 °C. The results of this study can promote the utilization of waste wind blades in the production of sustainable building materials.
期刊：
Nondestructive Testing and Evaluation,2024年 ISSN：1058-9759
通讯作者：
Liu, HW
作者机构：
[He, Dongliang; Cheng, Yanhui] Hunan City Univ, Sch Civil Engn, Yiyang, Hunan, Peoples R China.;[Liu, Hongwei] Cent South Univ, Sch Resources & Safety Engn, Changsha, Hunan, Peoples R China.;[Wang, Guoxian] Yunnan Agr Univ, Coll Architecture & Engn, Kunming, Yunnan, Peoples R China.
通讯机构：
[Liu, HW ] C;Cent South Univ, Sch Resources & Safety Engn, Changsha, Hunan, Peoples R China.
关键词：
Rock materials;shear strength parameters;internal friction angle;cohesion;machine learning;artificial neural network
摘要：
Cohesion and internal friction angle are critical parameters for evaluating the suitability of stone. To build a reliable model to predict the cohesion and internal friction angle of rock, dataset containing 597 rock samples were collected and their petrological characteristics were investigated. In this study, artificial neural network (ANN) and particle swarm optimisation (PSO) algorithm are hybridised to establish a new hybrid machine learning (ML) model for predicting cohesion and internal friction angle based on petrological features. By comparing other five ML models, the efficiency of this model are assessed. Four statistical metrics such as correlation coefficient (R 2 ) and root-mean-square error (RMSE) were used to evaluate the model. The results show that the hybridisation of ANN and PSO significantly improves the prediction and generalisation ability of the hybrid ML model. Compared with other models, this model was the most effective model for predicting cohesion and internal friction angle, with R 2 value of 0.976 (Cohesion), 0.942 (Internal friction angle), RMSE of 0.697 (Cohesion), 0.935 (Internal friction angle). In addition, feature importance analysis showed that density and P-wave velocity were the most influential factors on cohesion, and uniaxial compressive strength, tensile strength and P-wave velocity were the most influential factors on internal friction angle.
期刊：
Geotechnical and Geological Engineering,2024年42(8):7385-7405 ISSN：0960-3182
通讯作者：
Da Hu
作者机构：
[Shurong Feng] Hunan Provincial Key Laboratory of Key Technology on Hydropower Development, PowerChina Zhongnan Engineering Corporation Limited, Changsha, China;[Yongjia Hu; Rong Hu] School of Civil Engineering, Hunan City University, Yiyang, China;Hunan Engineering Research Centre for Structural Safety and Disaster Prevention of Urban Underground Infrastructure, Hunan City University, Yiyang, China;[Da Hu; Yongsuo Li; Ze Tan] Hunan Engineering Research Centre for Structural Safety and Disaster Prevention of Urban Underground Infrastructure, Hunan City University, Yiyang, China<&wdkj&>School of Civil Engineering, Hunan City University, Yiyang, China
通讯机构：
[Da Hu] H;Hunan Engineering Research Centre for Structural Safety and Disaster Prevention of Urban Underground Infrastructure, Hunan City University, Yiyang, China<&wdkj&>School of Civil Engineering, Hunan City University, Yiyang, China
关键词：
Intelligent classification of surrounding rock;Stability of surrounding rock;Deep learning;Extreme learning machine;ISSA-ELM combined model
摘要：
During construction, mountainous highway tunnels are often subjected to complex forces and are prone to large deformations, which severely affect the long-term stability of the surrounding rock. Therefore, it is crucial to explore rapid identification and intelligent classification methods for rock surrounding tunnels. In response to the above issues, this study proposes a new intelligent classification and prediction method for rock surrounding highway tunnels on the basis of an index classification system of the environmental characteristics of rock surrounding highway tunnels combined with deep learning algorithms. This method can optimize the generative adversarial network for tabular data (CTGAN) via a genetic algorithm (GA) to increase the data volume and then use the Kolmogorov–Smirnov (K–S) test to determine the optimal parameters and samples in the CTGAN with a small number of samples. By combining the sparse search algorithm (SSA) and extreme learning machine (ELM) to construct the SSA-ELM model, the Singer mapping method is used to handle the sparrow random initialization problem, the parameters of the SSA-ELM model are further optimized via the K-fold cross-validation method, and the ISSA-ELM combination model is established. Finally, on the basis of actual engineering cases, 160 sets of data were optimized to analyse and evaluate the classification of surrounding rocks, verifying the rationality and effectiveness of the model. The research results show that the ISSA-ELM combination model minimizes the negative impact of subjective factors on the model and has the advantages of extremely low error, accurate stability, and high robustness. This can provide an important reference for predicting the stability of the surrounding rock in mountain road tunnels.
期刊：
INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS,2024年22(04):2450066 ISSN：0219-8762
通讯作者：
Ni, Pengpeng;Chen, Y
作者机构：
[Xiang, Xuejuan; Tan, Ze; Liu, Jing; Hu, Rong; Hu, Da; Hu, Yongjia; Li, Yongsuo] Hunan City Univ, Hunan Engn Res Ctr Struct Safety & Disaster Preven, Yiyang 413000, Peoples R China.;[Hu, Da; Li, Yongsuo] Hunan City Univ, Sch Civil Engn, Yiyang 413000, Peoples R China.;[Ni, Pengpeng; Ni, PP; Chen, Yu] Sun Yat Sen Univ, Sch Civil Engn, Guangzhou 510275, Peoples R China.;[Ni, Pengpeng; Ni, PP; Chen, Yu] State Key Lab Tunnel Engn, Guangzhou 510275, Peoples R China.;[Ni, Pengpeng; Ni, PP; Chen, Yu] Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China.
通讯机构：
[Ni, PP; Chen, Y ] S;Sun Yat Sen Univ, Sch Civil Engn, Guangzhou 510275, Peoples R China.;State Key Lab Tunnel Engn, Guangzhou 510275, Peoples R China.;Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China.
关键词：
Settlement prediction;finite element simulation;intelligent optimization algorithm;mesh optimization;finite element-machine learning coupling model
摘要：
Ground settlement prediction for shield construction is highly important and challenging. This study introduces a machine learning algorithm combined with finite element numerical simulation, i.e., machine learning–finite element mesh optimization. For surface subsidence prediction, 16 combination models of ANN, KNN, RF and SVR were optimized by PSO, GA, BT and BO, involving raw data preprocessing, principal component analysis, hyperparameter selection and prediction accuracy evaluation. A subway shield tunneling project was analyzed, in which the meshes of finite element numerical models were discretized into different sizes from 1.0[Formula: see text]m to 2.0[Formula: see text]m. In total, 360 sets of data points were extracted from the simulation results, including stress, strain, shield jacking force, internal friction angle, cohesion force, and settlement, of which 252 data points were used as the input parameters of machine learning model. Analysis of average error rate of finite element–machine learning coupling models showed that the finite element model had the highest accuracy of settlement prediction when the mesh size of the finite element model was 1.4[Formula: see text]m, and the GA-SVR model had the highest accuracy and generalization ability in ground settlement prediction. This study highlights the uniqueness of machine learning–finite element mesh optimization model in application.
摘要：
A novel fabricated steel structure exterior beam-to-column connection with knee braces is presented in this study, to improve the seismic performance and internal force distribution of semi-rigid connections. A design methodology for the new knee brace system was developed based on force equilibrium and displacement compatibility principles, recommending a range of shear coefficient values suitable for various joint internal force requirements. Moreover, three external beam-to-column connections were developed, produced, and examined under cyclic reversed loads. These include two knee-braced connection specimens with varying design parameters, as well as one comparison connection specimen. The experimental results indicate that the knee braces significantly enhance the stiffness and strength of the beam-column connections. Compared with the comparison specimen, the average peak bearing capacity of the specimens with square steel tube-knee brace (SST-KB) and angle steel-knee brace (AS-KB) increased by 1.39 times and 1.63 times, respectively, and the initial stiffness increased by 2.37 times and 2.18 times, respectively. Stress from the beam flanges is effectively redistributed to the outside of the knee braces, providing better protection for the joint area. Failure modes such as knee brace fracture, knee brace compressive buckling, and local buckling of the beam flanges are observed. The specimen with AS-KB demonstrates better seismic performance than the SST-KB, showing superior potential for stable energy dissipation. When loaded to 64 mm, the cumulative energy dissipation of the two specimens with SST-KB and AS-KB increased by 34.5 % and 81.9 %, respectively, compared to the comparison specimen. The AS-KB effectively controls the progression of component damage, slows the degradation of overall strength, and facilitates the functional recovery of the connection system. This experimental study provides valuable references for designing and applying fabricated semi-rigid steel frames.
A novel fabricated steel structure exterior beam-to-column connection with knee braces is presented in this study, to improve the seismic performance and internal force distribution of semi-rigid connections. A design methodology for the new knee brace system was developed based on force equilibrium and displacement compatibility principles, recommending a range of shear coefficient values suitable for various joint internal force requirements. Moreover, three external beam-to-column connections were developed, produced, and examined under cyclic reversed loads. These include two knee-braced connection specimens with varying design parameters, as well as one comparison connection specimen. The experimental results indicate that the knee braces significantly enhance the stiffness and strength of the beam-column connections. Compared with the comparison specimen, the average peak bearing capacity of the specimens with square steel tube-knee brace (SST-KB) and angle steel-knee brace (AS-KB) increased by 1.39 times and 1.63 times, respectively, and the initial stiffness increased by 2.37 times and 2.18 times, respectively. Stress from the beam flanges is effectively redistributed to the outside of the knee braces, providing better protection for the joint area. Failure modes such as knee brace fracture, knee brace compressive buckling, and local buckling of the beam flanges are observed. The specimen with AS-KB demonstrates better seismic performance than the SST-KB, showing superior potential for stable energy dissipation. When loaded to 64 mm, the cumulative energy dissipation of the two specimens with SST-KB and AS-KB increased by 34.5 % and 81.9 %, respectively, compared to the comparison specimen. The AS-KB effectively controls the progression of component damage, slows the degradation of overall strength, and facilitates the functional recovery of the connection system. This experimental study provides valuable references for designing and applying fabricated semi-rigid steel frames.
摘要：
Timely and accurate detection and identification of cavities under urban roads is the key to road safety. Due to the inability to obtain accurate migration velocity and the difficulty of achieving complete convergence of diffraction signals of the cavity disease, traditional migration methods struggle to accurately identify and locate the subgrade cavity. This paper proposes a GPR image migration processing (TUFK method) based on 2D undecimated wavelet transform and the F-K method in accordance with the high-precision imaging of the subgrade cavity. The finite-difference forward models of subgrade cavity without and with noise are established, and the model test of cavity detection by GPR is carried out in the laboratory. Through the fine extraction and migration processing of the weak diffraction signals from the cavity, the optimal velocity required for migration is analyzed, and the TUFK method is applied to the migration process of GPR data acquired for the purpose of cavity detection. Furthermore, the proposed method is applied to the processing of GPR data acquired in the field with a cavity below the roadbed. The results show that the TUFK method can accurately extract the diffraction signals from the cavity and achieve the fine convergence of cavity diffraction signals whether in noiseless or noisy environments. Compared with the traditional Kirchhoff and F-K migration methods, this method can effectively obtain accurate migration velocity and the migration results can reflect the actual position and shape of the cavity. This study can provide a new idea and effective method for the imaging of subgrade cavity.
作者机构：
[Huang, Yonggang] Hunan City Univ, Coll Civil Engn, Yiyang, Hunan, Peoples R China.;[Huang, Yonggang] Hunan City Univ, Hunan Engn Res Ctr Dev & Applicat Ceramsite Concre, Yiyang, Hunan, Peoples R China.;[Huang, Yonggang] Hunan City Univ, Key Lab Green Bldg & Intelligent Construct Higher, Yiyang, Hunan, Peoples R China.;[Wang, Guiyao; Deng, Peng] Changsha Univ Sci & Technol, Coll Civil Engn, Changsha, Hunan, Peoples R China.;[Zhang, Hongri] Guangxi Commun Grp Co LTD, Nanning, Guangxi, Peoples R China.
通讯机构：
[Huang, YG ] H;Hunan City Univ, Coll Civil Engn, Yiyang, Hunan, Peoples R China.;Hunan City Univ, Hunan Engn Res Ctr Dev & Applicat Ceramsite Concre, Yiyang, Hunan, Peoples R China.;Hunan City Univ, Key Lab Green Bldg & Intelligent Construct Higher, Yiyang, Hunan, Peoples R China.
关键词：
Cracking;Expansive soil;Test;Vetiver root
摘要：
The study investigated the reinforcing effect of vetiver root on soil by conducting outdoor planting tests and indoor root tests. The cracking indexes of soil specimens with varying root contents were analyzed, and a statistical model was established to determine the relationship between the cracking indexes, the number of dry and wet cycles, and the root content. The study revealed the crack evolution law of vetiver-reinforced expansive soil. The study explored the mechanism of the vegetation root in inhibiting the cracking of expansive soil and determined the optimal planting density of vetiver grass through outdoor planting tests. The results indicate that: The surface crack rate (CR), total crack length (CL), and crack number (CN) in the root-soil specimen exhibited exponential growth with an increase in the number of wet and dry cycles. This growth was more pronounced during the first and second cycles. The vetiver root could effectively reduce soil crack formation, and the specimen's cracking resistance is positively correlated with the root content. With the root content increased, the CR, CN, and CL decreased. The logistic model is suited to the CL of added root soil. The logistic model is more suitable for the growth model of the CR of the expansive soil with low root content, while the Boltzmann model is more suitable for the growth model of the CR of the expansive soil with high root content. Width of crack (CW) is better suited to the DoseResp growth model. The Boltzmann model is more applicable to the CN in expansive soils with low reinforcement, while the logistic growth model is more suitable for the development of CN above 0.21% root content. The development of the crack network was influenced by two key factors: the root content and the number of wet and dry cycles. Under the condition of planting roots, the development of crack networks in expansive soil differs from that of expansive soil with added roots, and there is no clear pattern to follow. The inhibitory effect of the vetiver root on cracking of expansive soil is related to the planting density of vetiver.
作者机构：
[Xiao, Eguo; Tan, Xianliang; He, Zhengyi; Xiang, Yi; Li, Linshu] School of Civil Engineering, Hunan City University, Yiyang 413000, China;School of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China;Key Laboratory of Key Technologies of Digital Urban-Rural Spatial Planning of Hunan Province, Yiyang 413000, China;Key Laboratory of Urban Planning Information Technology of Hunan Provincial Universities, Yiyang 413000, China;[Yi, Chun] School of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China<&wdkj&>Key Laboratory of Key Technologies of Digital Urban-Rural Spatial Planning of Hunan Province, Yiyang 413000, China<&wdkj&>Key Laboratory of Urban Planning Information Technology of Hunan Provincial Universities, Yiyang 413000, China
摘要：
This study aims to address the complexities in the calculation of the tangent stiffness matrix and the issues of divergence in iterative calculations in the shape-finding process of existing suspension bridge main cables. The research investigates the factors influencing the computational errors of existing cable element theories and the convergence or divergence of the main cable shape-finding calculations. First, a nonlinear equation for calculating the height of the cable element is constructed. Subsequently, a formula for cable height calculation is established according to the differential equations of the deformed cable element. Finally, considering the mass conservation principle before and after the cable deformation, a nonlinear system of equations for the configuration of the cable element is derived. Given the symmetric nature of the mid-span structure and loading in most suspension bridges, it is inferred that the point of the lowest slope of the main cable in the completed bridge state serves as the symmetry center of the structure. Consequently, a symmetric main cable shape-finding method is developed. A comparative analysis between the proposed method and existing iterative methods was conducted in terms of calculation accuracy and convergence behavior. The results indicate that the difference in horizontal cable force at the IP point between the two methods is 1.9 kN, and the difference in unstressed length is 2.5 mm. The calculation efficiency of the symmetric main cable shape-finding method is more than twice that of traditional iterative algorithms, with the number of iterations required for convergence generally being lower than that of traditional methods. For initial values that cause divergence in traditional iterative methods, the symmetric main cable shape-finding method achieved convergence within 10 iterations. The derived cable element theory and the symmetric main cable shape-finding calculation method can lay a theoretical foundation for the refined and efficient calculation of the main cable shape-finding process.
期刊：
Bulletin of Engineering Geology and the Environment,2024年83(3):1-15 ISSN：1435-9529
通讯作者：
Hu, HX;Deng, C
作者机构：
[Xie, Zhongliang; Cai, Yuehui; Hu, Huanxiao; Lu, Yufan; Gan, Benqing] Minist Educ, Key Lab Metallogen Predict Nonferrous Met & Geol E, Changsha 410083, Peoples R China.;[Xie, Zhongliang; Cai, Yuehui; Hu, Huanxiao; Lu, Yufan; Gan, Benqing] Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China.;[Deng, Chao] Hunan City Univ, Coll Civil Engn, Yiyang 413000, Peoples R China.
通讯机构：
[Deng, C ] H;[Hu, HX ] M;Minist Educ, Key Lab Metallogen Predict Nonferrous Met & Geol E, Changsha 410083, Peoples R China.;Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China.;Hunan City Univ, Coll Civil Engn, Yiyang 413000, Peoples R China.
摘要：
The grouting technique has been widely applied in geotechnical engineering. However, due to the concealed nature of underground engineering, there is relatively limited research on the diffusion characteristics of grout in sandy soil. This study utilized a self-developed three-dimensional grouting model test system. Experiments tests were conducted using standard sand and different water-cement (w/c) ratios of grout under limited boundary conditions, diffusion mechanism of sand soil grouting revealed. It was found that lower w/c ratios require higher grouting pressure when injecting grout into the sandy soil. The grouting pressure exhibited a pulsating pattern over time and a grouting pressure up to 323.9 kPa at w/c = 0.6. The grouting diffusion pattern under all w/c ratio conditions displayed typical columnar diffusion, and there was a good linear relationship between the average diffusion radius of the grout and the w/c ratio; the average diffusion radius was between 121 and 208 mm. The volume of the grout bulbs varied significantly at different w/c ratios, decreasing as the w/c ratio increased. With increased grouting pressure and w/c ratio, the dewatering effect during the grouting process became more pronounced, and the bleeding rate of the slurry with w/c = 1.4 (67.1%) is more than six times that of the w/c = 0.6 (10.5%). The results show that the uplift displacement on the soil surface exhibited certain hysteresis, and the grouting lifting process in sandy soil was divided into three stages: initial deformation, accelerated uplift, and stable uplift.
摘要：
Reasonable prediction of concrete creep is the basis of studying long-term deflection of concrete structures. In this paper, a hybrid model-driven and data-driven (HMD) method for predicting concrete creep is proposed by using the sequence integration strategy. Then, a novel uncertainty prediction model (UPM) is developed considering uncertainty quantification. Finally, the effectiveness of the proposed method is validated by using the North-western University (NU) database of creep, and the effect of uncertainty on prediction results are also discussed. The analysis results show that the proposed HMD method outperforms the model-driven and three data-driven methods, including the genetic algorithm-back propagation neural network (GA-BPNN), particle swarm optimization-support vector regression (PSO-SVR) and convolutional neural network only method, in accuracy and time efficiency. The proposed UPM of concrete creep not only ensures relatively good prediction accuracy, but also quantifies the model and measurement uncertainties during the prediction process. Additionally, although incorporating measurement uncertainty into concrete creep prediction can improve the prediction performance of UPM, the prediction interval of the creep compliance is more sensitive to model uncertainty than to measurement uncertainty, and the mean contribution of variance attributed to the model uncertainty to the total variance is about 90%.
期刊：
steel research international,2024年 ISSN：1611-3683
通讯作者：
Tang, H;Peng, JX
作者机构：
[Tang, Huang; Yang, Yiming; Li, Hai] Hunan City Univ, Key Lab Green Bldg & Intelligent Construct Higher, Yiyang 413000, Peoples R China.;[Tang, Huang; Yang, Yiming; Li, Hai] Hunan City Univ, Hunan Engn Res Ctr Dev & Applicat Ceramsite Concre, Yiyang 413000, Hunan, Peoples R China.;[Peng, Jianxin; Peng, Hui] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Hunan, Peoples R China.;[Zheng, Liangfei; Chen, Zexiang; Gao, Qiong; Yang, Zonggui] Hunan City Univ, Sch Civil Engn, Yiyang 413000, Hunan, Peoples R China.;[Xiao, Linfa] East China Jiaotong Univ, Sch Transportat Engn, Nanchang 330013, Jiangxi, Peoples R China.
通讯机构：
[Peng, JX ] C;[Tang, H ] H;Hunan City Univ, Key Lab Green Bldg & Intelligent Construct Higher, Yiyang 413000, Peoples R China.;Hunan City Univ, Hunan Engn Res Ctr Dev & Applicat Ceramsite Concre, Yiyang 413000, Hunan, Peoples R China.;Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Hunan, Peoples R China.
关键词：
flexural behavior;flexural strength model;high-performance steel;local corrosion;pure bending zone
摘要：
Four high‐performance steel (HPS) beams are designed, the flexural bearing capacity test is carried out. The theoretical flexural strength of the corroded HPS beam under different failure modes is obtained, which is compared with the simulation results. Finally, the residual flexural properties of corroded HPS beams under different corrosion conditions are studied. This article undertakes the tested and theoretical investigations on the flexural behaviors of high‐performance steel (HPS) beams subjected to local corrosion in pure bending zone. Four H‐type HPS beams are designed, then the flexural bearing capacity test is carried out. Mechanical behaviors, including load‐deflection response, strain development, and failure modes are investigated in detail. A simple theoretical model of flexural strength is proposed. The tested and theoretical results show that: with the increase of corrosion rate, the random distribution of the residual section of HPS beam tends to be stable, the corrosion position also plays an important role in the random distribution of the residual area. The corrosion position has a greater influence on the residual flexural strength of corroded HPS beam than the corrosion degree. The theoretical flexural strength model can slight accurately predict the flexural strength of corroded H‐type HPS beam, the error is less than 5%. The corrosion of compressive flange has the most serious effect on the flexural strength degradation of HPS beams, followed by the corrosion of tensile flange and web corrosion. The influence of corrosion in the web height direction on the flexural strength of corroded HPS beam is extremely limited.
摘要：
In this study, we propose an accurate gain method for ground-penetrating radar (GPR) signals based on the characteristics of refined time-frequency analysis and translation invariance offered by the Stationary Wavelet Packet Transform (SWPT), combined with the conventional signal gain approach. This method aims to address the issue of low signal resolution resulting from the direct gain processing of GPR signals with a low signal-to-noise ratio (SNR). Specifically, the GPR signals are initially decomposed into appropriate wavelet packet coefficients using SWPT, wherein only those coefficients with high SNR undergo gain processing, followed by reconstruction of the signals through SWPT. By employing accurate gain processing on low SNR GPR signals acquired during concrete crack detection tests, we have confirmed that the proposed method effectively distinguishes the target reflected signals from most noise, thereby achieving accurate amplification of the desired reflected signals and significantly enhancing the GPR signals resolution under low SNR conditions.
In this study, we propose an accurate gain method for ground-penetrating radar (GPR) signals based on the characteristics of refined time-frequency analysis and translation invariance offered by the Stationary Wavelet Packet Transform (SWPT), combined with the conventional signal gain approach. This method aims to address the issue of low signal resolution resulting from the direct gain processing of GPR signals with a low signal-to-noise ratio (SNR). Specifically, the GPR signals are initially decomposed into appropriate wavelet packet coefficients using SWPT, wherein only those coefficients with high SNR undergo gain processing, followed by reconstruction of the signals through SWPT. By employing accurate gain processing on low SNR GPR signals acquired during concrete crack detection tests, we have confirmed that the proposed method effectively distinguishes the target reflected signals from most noise, thereby achieving accurate amplification of the desired reflected signals and significantly enhancing the GPR signals resolution under low SNR conditions.
