Query：
学者姓名：赖大德
Refining：
Year
Type
Indexed by
Source
Complex
Co-Author
Language
Clean All
Abstract ：
This study investigates the cyclic performance of circular concrete-filled thin-walled steel tubular (CFST) composite joints having circumferential gaps, focusing on the reduction of steel tube thickness and novel reinforcement strategies using a CFRP-annular stiffener system. Experimental tests and finite element analysis (FEA) were conducted on four large-scale specimens to evaluate hysteretic behavior, energy dissipation, failure patterns, and interfacial interactions. Results revealed that reducing steel tube wall thickness decreased ultimate strength by 20-25%. The hybrid CFRP-annular stiffener system effectively mitigated these deficiencies, enhancing yield load capacity by 50% and cumulative energy dissipation by around 40% compared to unreinforced joints. This system redistributed stress concentrations, suppressed shear deformation, delayed buckling, and shifted failure locations from catastrophic joint-core shear failure to ductile beam flange/web fractures. Parametric FEA confirmed the system's robustness under combined adverse conditions, restoring 80% of lost load capacity relative to thicker-tube benchmarks. A predictive model quantified reinforcement efficacy, showing dependence on CFRP layers and stiffener thickness rather than steel strength. The study bridges experimental and numerical insights, proposing practical retrofitting guidelines for defective CFST structures in seismic regions.
Keyword ：
Annular stiffeners Annular stiffeners CFRP wraps CFRP wraps CFST composite joints CFST composite joints Circumferential gaps Circumferential gaps Cyclic loading Cyclic loading
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lai, Dade , Cui, Yucheng , Liao, Feiyu et al. Cyclic performance of novel strengthened CFST composite joints with circumferential gaps [J]. | JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH , 2026 , 236 . |
| MLA | Lai, Dade et al. "Cyclic performance of novel strengthened CFST composite joints with circumferential gaps" . | JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 236 (2026) . |
| APA | Lai, Dade , Cui, Yucheng , Liao, Feiyu , Lu, Feng . Cyclic performance of novel strengthened CFST composite joints with circumferential gaps . | JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH , 2026 , 236 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Concrete Filled Steel Tube (CFST) is favorable to utilize in the construction of arch bridges. Since CFST are mostly intended to carry compression loads, the axial compressive capacity is of primary importance in the of CFST columns. Current standards limit their applicability to conventional material strength and geometric dimensions of CFST, sometimes failing to meet the requirements of modern arch bridges. This study has developed a probabilistic Machine Learning (ML) model based on the NGBoost algorithm. The results demonstrate that the NGBoost model with a LogNormal distribution provides both accurate and probabilistic predictions, surpassing the performance of the XGBoost model and the NGBoost model with a Normal distribution. Furthermore, we have employed the SHapley Additive exPlanations (SHAP) method to interpret the probabilistic model. It was revealed that the column dimensions exert the most significant influence on the axial compressive capacity of CFST.
Keyword ：
Arch bridge Arch bridge Axial capacity Axial capacity CFST columns CFST columns Probabilistic model Probabilistic model
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lai, Dade , Demartino, Cristoforo , Xue, Junqing et al. Probabilistic Machine Learning Prediction for Axial Compressive Capacity of CFST: Preliminary Results [J]. | PROCEEDINGS OF ARCH 2023, VOL 1 , 2025 , 33 : 141-149 . |
| MLA | Lai, Dade et al. "Probabilistic Machine Learning Prediction for Axial Compressive Capacity of CFST: Preliminary Results" . | PROCEEDINGS OF ARCH 2023, VOL 1 33 (2025) : 141-149 . |
| APA | Lai, Dade , Demartino, Cristoforo , Xue, Junqing , Contento, Alessandro , Briseghella, Bruno . Probabilistic Machine Learning Prediction for Axial Compressive Capacity of CFST: Preliminary Results . | PROCEEDINGS OF ARCH 2023, VOL 1 , 2025 , 33 , 141-149 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Accurate characterization of mean wind profiles is essential for optimizing the design of structures and operation of wind energy systems. This study presents a novel approach for the identification of mean logarithmic wind profiles using wind lidar measurements. The identification of mean logarithmic wind profiles incorporating the stability correction term is accomplished through a two-step approach that involves transforming the wind profile into a nondimensional form. Initially, wind profiles are segmented into sectors, and separate analyses are conducted for each sector. First, an extreme value analysis is conducted, and the aerodynamic roughness length is determined through a statistical analysis of the values derived from fitting mean logarithmic wind profiles with zero stability coefficient terms. In the second stage, the aerodynamic roughness length is held constant based on the results from the previous step, while the Monin-Obukhov length is fitted to all the profiles. During this process, it was observed that the nondimensional form of the wind profile in unstable atmospheric conditions exhibits multiple solutions. To resolve this issue, a defined constraint on the Monin-Obukhov length was introduced to guarantee a singular solution. Ultimately, the identification of mean logarithmic wind profiles was carried out, taking into account the stability correction term with this constraint in place. An application of the proposed method is demonstrated using measurements from a coastal area, specifically Lamezia Terme, Italy, with a dataset encompassing approximately 4 years of 10-min wind profiles. Statistical analyses and a probabilistic model for mean wind profiles are also presented. The application of the proposed method yielded promising results, showcasing its effectiveness in probabilistic characterizing wind profiles for structural and energy applications.
Keyword ：
Logarithmic wind profiles Logarithmic wind profiles Mean wind profile identifications Mean wind profile identifications Stability conditions Stability conditions Wind lidar measurements Wind lidar measurements
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wei, Jingyu , Lai, Dade , Xu, Yongjia et al. Characterization of Vertical Wind Profiles Using a Ground-Based Lidar Station in an Open Flat Terrain [J]. | JOURNAL OF STRUCTURAL ENGINEERING , 2025 , 151 (10) . |
| MLA | Wei, Jingyu et al. "Characterization of Vertical Wind Profiles Using a Ground-Based Lidar Station in an Open Flat Terrain" . | JOURNAL OF STRUCTURAL ENGINEERING 151 . 10 (2025) . |
| APA | Wei, Jingyu , Lai, Dade , Xu, Yongjia , Gulli, Daniel , Calidonna, Claudia Roberta , Demartino, Cristoforo . Characterization of Vertical Wind Profiles Using a Ground-Based Lidar Station in an Open Flat Terrain . | JOURNAL OF STRUCTURAL ENGINEERING , 2025 , 151 (10) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
使用多轴加载系统对UHPC钢管混凝土叠合柱进行了拟静力试验，并将钢管混凝土叠合柱和钢管混凝土柱作为对比，研究了各试件在低周往复荷载下的滞回性能、刚度退化和耗能能力，并分析了UHPC钢管混凝土叠合柱的损伤和失效特征。试验结果表明：UHPC钢管混凝土叠合柱在往复荷载下表现出远高于混凝土钢管叠合柱和钢管混凝土柱的初始刚度，且即使刚度退化后仍高于钢管混凝土柱。同时，其在屈服前的承载能力退化水平较低，损伤程度较小。然而，由于外围UHPC的存在，限制了塑性铰的发展，导致极限位移减小，塑性变形能力较低。
Keyword ：
UHPC钢管混凝土叠合柱 UHPC钢管混凝土叠合柱 抗震性能 抗震性能 破坏形态 破坏形态 钢管混凝土 钢管混凝土
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 林牧新 , 赖大德 , 廖飞宇 et al. UHPC包覆钢管混凝土叠合柱抗震性能试验研究 [J]. | 福建建设科技 , 2025 , 4 (01) : 16-18,31 . |
| MLA | 林牧新 et al. "UHPC包覆钢管混凝土叠合柱抗震性能试验研究" . | 福建建设科技 4 . 01 (2025) : 16-18,31 . |
| APA | 林牧新 , 赖大德 , 廖飞宇 , 杨昱幸 , 陈宇峰 , 刘建军 . UHPC包覆钢管混凝土叠合柱抗震性能试验研究 . | 福建建设科技 , 2025 , 4 (01) , 16-18,31 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
This study develops an advanced probabilistic machine learning (ML) framework to predict failure modes and axial load-bearing capacity of rectangular concrete-filled steel tube (CFST) columns, addressing critical challenges of class imbalance and prediction uncertainty. Leveraging a comprehensive dataset of 597 experimental samples, a rigorous feature selection procedure is conducted to optimize the ML applications. Three ML models are developed and evaluated: a deterministic approach (Random Forest) and two probabilistic models (Gaussian Process and Natural Gradient Boosting, NGBoost). To address the issue of class imbalance in failure mode classification, a hybrid resampling strategy combining the Synthetic Minority Over-sampling Technique and Tomek Links method (SMOTE-Tomek) is implemented. Furthermore, A novel classification-regression cascaded framework is proposed, where failure modes are first classified, followed by category-specific regression for capacity prediction. Results demonstrate that SMOTE-Tomek significantly improves minority failure mode classification, increasing the average F1-scores by 8.1 % (for flexural failure) and 118.2 % (for combined failure). The cascaded framework outperforms direct regression in estimating capacity, achieving a 56 % higher coefficient of determination (R2) and 15-30 % lower error metrics on average. NGBoost excels in both probabilistic failure mode prediction and uncertainty-aware capacity estimation, enabling reliability-based design and riskinformed decision-making for CFST structures in engineering practice.
Keyword ：
Axial compression Axial compression Classification-regression Classification-regression Concrete-filled steel tube column Concrete-filled steel tube column Failure mode Failure mode Load-bearing capacity Load-bearing capacity Machine learning Machine learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhong, Xueqi , Lai, Dade , Yu, Qiyao et al. Probabilistic machine learning with a cascaded framework for robust failure mode classification and capacity estimation of rectangular concrete-filled steel tube columns under axial compression [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 162 . |
| MLA | Zhong, Xueqi et al. "Probabilistic machine learning with a cascaded framework for robust failure mode classification and capacity estimation of rectangular concrete-filled steel tube columns under axial compression" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 162 (2025) . |
| APA | Zhong, Xueqi , Lai, Dade , Yu, Qiyao , Liao, Feiyu . Probabilistic machine learning with a cascaded framework for robust failure mode classification and capacity estimation of rectangular concrete-filled steel tube columns under axial compression . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 162 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
This study aims to investigate the connection behavior of bamboo scrimber beam with a slotted-in steel plate and multi-bolt under the load perpendicular to the grain. In total, 27 specimens were categorized into 9 groups with varying bolt edge distances, number of bolts, bolt end distances, and total lengths of joints. The test results demonstrate that the ratio of edge distance to row spacing and the number of bolt rows significantly influenced the cracking mode, cracking load, and ultimate load. Both the bolt end distance and the total length had minimal impact on the mechanical performance of connections, as variations in these two parameters resulted in changes of only -0.22% to 3.48% in the cracking load and -0.64% to 0.62% in the ultimate load. Furthermore, a modified Van der Put model was proposed and validated, demonstrating good performance in predicting the failure mode of multi-bolt connections. It is recommended to increase the number of bolt rows to at least three and maintain the ratio of edge distance to row spacing greater than one, in order to prevent premature failure of connections.
Keyword ：
Bamboo scrimber beam Bamboo scrimber beam Experimental investigation Experimental investigation Load perpendicular to the grain Load perpendicular to the grain Multi-bolt connections Multi-bolt connections Slotted-in steel plate Slotted-in steel plate
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Luo, Shuihua , Lai, Dade , Liao, Feiyu et al. Experimental performance of bamboo scrimber beam connections with slotted-in steel plate and multi-bolt under the load perpendicular to grain [J]. | STRUCTURES , 2025 , 80 . |
| MLA | Luo, Shuihua et al. "Experimental performance of bamboo scrimber beam connections with slotted-in steel plate and multi-bolt under the load perpendicular to grain" . | STRUCTURES 80 (2025) . |
| APA | Luo, Shuihua , Lai, Dade , Liao, Feiyu , Yang, Yuxing , Zhang, Weijie . Experimental performance of bamboo scrimber beam connections with slotted-in steel plate and multi-bolt under the load perpendicular to grain . | STRUCTURES , 2025 , 80 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
This study explores the dynamic lateral impact performance of Concrete-Filled Steel Tubular (CFST) columns affected by pitting corrosion and strengthened with Carbon Fiber Reinforced Polymer (CFRP). A total of 24 CFST specimens were fabricated and tested, including 9 unstrengthened columns and 15 columns reinforced with varying layers of CFRP wraps. Key variables included corrosion pit depth, pit density, and CFRP layers. Experimental and Finite Element Analysis (FEA) results revealed that pitting corrosion significantly reduces the stiffness and impact resistance of CFST columns by weakening the confinement effect of the steel tube and introducing local stress concentrations. CFRP strengthening effectively mitigates these adverse effects by enhancing stiffness, reducing displacement, and improving the interaction between the steel tube and concrete core. Notably, the benefits of CFRP become more significant with increasing corrosion severity and impact energy. Based on the experimental findings, a simplified design method is proposed to estimate the impact resistance of corroded CFST columns using only their static axial load-bearing capacity and the input kinetic energy, offering a practical approach for engineering assessment.
Keyword ：
CFRP strengthening CFRP strengthening CFST columns CFST columns Experimental tests Experimental tests Finite element analysis Finite element analysis Lateral impact loads Lateral impact loads Pitting corrosion damage Pitting corrosion damage
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liao, Feiyu , Huang, Xuankai , Lai, Dade et al. Lateral impact performance of pitting corroded CFST columns with CFRP strengthening [J]. | JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH , 2025 , 232 . |
| MLA | Liao, Feiyu et al. "Lateral impact performance of pitting corroded CFST columns with CFRP strengthening" . | JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 232 (2025) . |
| APA | Liao, Feiyu , Huang, Xuankai , Lai, Dade , Qiu, Hao . Lateral impact performance of pitting corroded CFST columns with CFRP strengthening . | JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH , 2025 , 232 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
This study examines the cyclic performance of circular concrete-filled steel tube (CFST) composite joints with circumferential gap imperfections, emphasizing the effects of varying gap ratios (0 % to 2.2 %), increased steel beam height (100 and 150 mm), and different steel tube thicknesses (3 and 4 mm). A combined experimental and numerical investigation was conducted on seven large-scale joint specimens to evaluate hysteretic response, energy dissipation capacity, failure mechanisms, stress distribution patterns, and interfacial behavior between steel tube and core concrete. The results demonstrate that the presence of circumferential gaps significantly compromises joint performance, reducing peak load capacity by up to 20 %, initial stiffness by approximately 10 %, and energy dissipation by over 10 %, while accelerating rigidity degradation. Circumferential gaps disrupt the stress transfer path from the steel tube to the core concrete, leading to severe stress concentration at the joint core and premature shear failure. Conversely, increasing the steel beam height enhances load-bearing capacity, improves energy dissipation, and delays stiffness degradation, particularly in defective joints. The increase in steel tube thickness offers moderate gains in shear resistance but limited improvements in global cyclic performance. A predictive model was developed to quantify the influence of circumferential gaps, showing good agreement with experimental tests. These findings underscore the critical role of steel-concrete interfacial integrity in CFST joint behavior and highlight effective structural strategies to mitigate the adverse effects of circumferential imperfections.
Keyword ：
CFST composite joints CFST composite joints Circumferential gaps Circumferential gaps Cyclic load Cyclic load Experimental tests Experimental tests Finite element analysis Finite element analysis
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liao, Feiyu , Cui, Yucheng , Zhong, Xueqi et al. Cyclic performance of CFST composite joints with circumferential gap imperfections [J]. | STRUCTURES , 2025 , 82 . |
| MLA | Liao, Feiyu et al. "Cyclic performance of CFST composite joints with circumferential gap imperfections" . | STRUCTURES 82 (2025) . |
| APA | Liao, Feiyu , Cui, Yucheng , Zhong, Xueqi , Lai, Dade , Yang, Dacheng . Cyclic performance of CFST composite joints with circumferential gap imperfections . | STRUCTURES , 2025 , 82 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The response of structures under rapidly varying loads can be affected by strain rate sensitivity generally expressed using Dynamic Increase Factor (DIF). Current models for estimating the DIF in Reinforced Concrete (RC) structures are generally deterministic and have restricted applicability due to their dependence on limited experimental data resulting in bias. This paper overcomes these limitations by proposing three probabilistic models that quantify compressive and tensile concrete and steel DIF, accounting for the relevant uncertainties. The proposed models are based on existing deterministic models with the addition of probabilistic correction terms. Bayesian updating is employed to estimate the unknown model parameters using observational data from a large collection of experimental observations. The models incorporate model uncertainties stemming from assumed model form and (potential) missing variables through a model error term. The proposed probabilistic models are used to evaluate the reliability of RC structures under dynamic loads. As an illustration, the proposed probabilistic models are used to estimate the reliability of an example RC column under combined dynamic axial force and moment, and a RC column or beam under dynamic bending moments resulting in cracking. In the two examples, we consider the ACI 318-19 requirements for Ultimate Limit State (ULS) and Serviceability Limit States (SLS). In comparison to deterministic DIF models, the proposed probabilistic models yield enhanced predictive accuracy, presenting a practical and robust approach to assess the structural reliability under impact and blast loads.
Keyword ：
Bayesian updating Bayesian updating Dynamic increase factor Dynamic increase factor Impact and blast loads Impact and blast loads Probabilistic models Probabilistic models RC structures RC structures
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lai, Dade , Nocera, Fabrizio , Demartino, Cristoforo et al. Probabilistic models of dynamic increase factor (DIF) for reinforced concrete structures: A Bayesian approach [J]. | STRUCTURAL SAFETY , 2024 , 108 . |
| MLA | Lai, Dade et al. "Probabilistic models of dynamic increase factor (DIF) for reinforced concrete structures: A Bayesian approach" . | STRUCTURAL SAFETY 108 (2024) . |
| APA | Lai, Dade , Nocera, Fabrizio , Demartino, Cristoforo , Xiao, Yan , Gardoni, Paolo . Probabilistic models of dynamic increase factor (DIF) for reinforced concrete structures: A Bayesian approach . | STRUCTURAL SAFETY , 2024 , 108 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The evaluation of the maximum displacement is pivotal for the application of the performance design of Concrete Filled Steel Tubes (CFSTs) under lateral impact loads. Traditional approaches have limitations in their predictive capabilities and necessitate substantial modeling efforts and computational resources, especially if employed for probabilistic predictions. In recent years, Machine Learning (ML) algorithms have been increasingly utilized to tackle complex problems involving extreme loads like impact and blast. However, a significant drawback of most ML models is their limitation in accounting for uncertainties in their outputs. This study endeavors to introduce a novel probabilistic ML model utilizing the algorithm known as NGBoost, which leverages gradient boosting to enable generic probabilistic predictions. The predictions obtained from NGBoost, using both Normal and LogNormal distributions, are compared with other deterministic models such as eXtreme Gradient Boosting ( XGBoost ), artificial neural network ( ANN ), and with a different probabilistic model, i.e., Gaussian Process Regression ( GPR ). In order to train and validate the models, a comprehensive database was compiled, consisting of 192 experimentally tested specimens. The results demonstrate that the probabilistic ML model achieves high accuracy and provides more detailed probabilistic predictions. Furthermore, the SHapley Additive exPlanations (SHAP) method is utilized to evaluate the relative importance of the input features and establish the relationship between the input features and the target output. Additionally, a comprehensive parametric study is conducted to explore the influence of each input feature. Finally, a simple application of the probabilistic model is presented. The proposed probabilistic ML model presents an application in performancebased design that boasts decreased computational demands and enhanced user -friendliness compared to conventional approaches.
Keyword ：
CFST columns CFST columns Lateral impact Lateral impact Machine learning Machine learning Maximum displacements Maximum displacements NGBoost NGBoost Probabilistic model Probabilistic model SHapley additive exPlanation (SHAP) SHapley additive exPlanation (SHAP)
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lai, Dade , Demartino, Cristoforo , Xiao, Yan . Probabilistic machine leaning models for predicting the maximum displacements of concrete-filled steel tubular columns subjected to lateral impact loading [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 135 . |
| MLA | Lai, Dade et al. "Probabilistic machine leaning models for predicting the maximum displacements of concrete-filled steel tubular columns subjected to lateral impact loading" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 135 (2024) . |
| APA | Lai, Dade , Demartino, Cristoforo , Xiao, Yan . Probabilistic machine leaning models for predicting the maximum displacements of concrete-filled steel tubular columns subjected to lateral impact loading . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 135 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
