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Effect of axial load and transverse reinforcements on the seismic performance of reinforced concrete

Mounir Ait BELKACEM, Hakim BECHTOULA, Nouredine BOURAHLA, Adel Ait BELKACEM

《结构与土木工程前沿（英文）》 2019年 第13卷 第4期   页码 831-851 doi: 10.1007/s11709-018-0513-3

摘要： The aim of this research is to assess the seismic performance of reinforced concrete columns under different axial load and transverse reinforcement ratios. These two parameters are very important as for the ductility, strength, stiffness, and energy dissipation capacity for a given reinforced concrete column. Effects of variable axial load ratio and transverse reinforcement ratio on the seismic performance of reinforced concrete columns are thoroughly analyzed. The finite element computer program Seismo-Structure was used to perform the analysis of series of reinforced concrete columns tested by the second author and other researchers. In order to reflect the reality and grasp the actual behavior of the specimens, special attention was paid to select the models for concrete, confined concrete, and steel components. Good agreements were obtained between the experimental and the analytical results either for the lateral force-drift relationships or for the damage progress prediction at different stages of the loading.

关键词： reinforced concrete columns     axial load     transverse reinforcement     ductility    

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Flexural and longitudinal shear performance of precast lightweight steel–ultra-high performance concrete composite beam

《结构与土木工程前沿（英文）》 2023年 第17卷 第5期   页码 704-721 doi: 10.1007/s11709-023-0941-6

摘要： In this study, the flexural and longitudinal shear performances of two types of precast lightweight steel–ultra-high performance concrete (UHPC) composite beams are investigated, where a cluster UHPC slab (CUS) and a normal UHPC slab (NUS) are connected to a steel beam using headed studs through discontinuous shear pockets and full-length shear pockets, respectively. Results show that the longitudinal shear force of the CUS is greater than that of the NUS, whereas the interfacial slip of the former is smaller. Owing to its better integrity, the CUS exhibits greater flexural stiffness and a higher ultimate bearing capacity than the NUS. To further optimize the design parameters of the CUS, a parametric study is conducted to investigate their effects on the flexural and longitudinal shear performances. The square shear pocket is shown to be more applicable for the CUS, as the optimal spacing between two shear pockets is 650 mm. Moreover, a design method for transverse reinforcement is proposed; the transverse reinforcement is used to withstand the splitting force caused by studs in the shear pocket and prevent the UHPC slab from cracking. According to calculation results, the transverse reinforcement can be canceled when the compressive strength of UHPC is 150 MPa and the volume fraction of steel fiber exceeds 2.0%.

关键词： precast steel–UHPC composite beam     flexural performance     longitudinal shear performance     parametric study     transverse reinforcement ratio    

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Detection for transverse corner cracks of steel plates’ surface using wavelet

Qiong ZHOU, Qi AN

《机械工程前沿（英文）》 2009年 第4卷 第2期   页码 224-227 doi: 10.1007/s11465-009-0017-x

摘要： An algorithm is presented for detecting transverse corner cracks at a steel plate surface using wavelet transform. According to characteristics of transverse corner crack images, the wavelet transform is used for the multi-scale analysis of detecting the image edges and disintegrating the image into four directions at the same time. The proper threshold value is chosen to segment the image into vertical components to obtain the final detection result. The experiment shows that transverse corner cracks of steel plates can be more effectively extracted by the proposed method than the other two common methods.

关键词： transverse corner cracks     defect detection     multi-scales wavelet analysis    

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Investigating the influence of delamination on the stiffness of composite pipes under compressive transverse

Sattar MALEKI, Roham RAFIEE, Abolfazl HASANNIA, Mohammad Reza HABIBAGAHI

《结构与土木工程前沿（英文）》 2019年 第13卷 第6期   页码 1316-1323 doi: 10.1007/s11709-019-0555-1

摘要： The effect of delamination on the stiffness reduction of composite pipes is studied in this research. The stiffness test of filament wound composite pipes is simulated using cohesive zone method. The modeling is accomplished to study the effect of the geometrical parameters including delamination size and its position with respect to loading direction on stiffness of the composite pipes. At first, finite element results for stiffness test of a perfect pipe without delamination are validated with the experimental results according to ASTM D2412. It is seen that the finite element results agree well with experimental results. Then the finite element model is developed for composite pips with delaminated areas with different primary shapes. Thus, the effect of the size of delaminated region on longitudinal and tangential directions and also its orientation with respect to loading direction on delamination propagation and stiffness reduction of the pipes is assessed.

关键词： delamination     composite pipes     stiffness test     cohesive zone method    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Punching of reinforced concrete slab without shear reinforcement: Standard models and new proposal

Luisa PANI, Flavio STOCHINO

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1196-1214 doi: 10.1007/s11709-020-0662-z

摘要： Reinforced concrete (RC) slabs are characterized by reduced construction time, versatility, and easier space partitioning. Their structural behavior is not straightforward and, specifically, punching shear strength is a current research topic. In this study an experimental database of 113 RC slabs without shear reinforcement under punching loads was compiled using data available in the literature. A sensitivity analysis of the parameters involved in the punching shear strength assessment was conducted, which highlighted the importance of the flexural reinforcement that are not typically considered for punching shear strength. After a discussion of the current international standards, a new proposed model for punching shear strength and rotation of RC slabs without shear reinforcement was discussed. It was based on a simplified load-rotation curve and new failure criteria that takes into account the flexural reinforcement effects. This experimental database was used to validate the approaches of the current international standards as well as the new proposed model. The latter proved to be a potentially useful design tool.

关键词： punching shear strength     reinforced concrete     slabs     reinforcement ratio    

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Spatial embedded reinforcement of 20-node block element for analysis PC bridges

LONG Peiheng, DU Xianting, CHEN Weizhen

《结构与土木工程前沿（英文）》 2008年 第2卷 第3期   页码 274-280 doi: 10.1007/s11709-008-0039-1

摘要： The formula for the contribution of prestressed reinforcement on embedded reinforcement element is derived according to the mechanical behavior of PC bridges and the foundational principle of finite element method. Mechanical concept is definite and examples validate the calculation results. Reinforcement element model allows generating a finite element mesh without taking into consideration the layout of reinforcements. Furthermore, the prestressing tendon may pass through the concrete elements in an arbitrary manner. It is an effective approach that the no-node loads are diverted from the tendons to the adjacent concrete elements. A useful arithmetic analysis of the spatial curved tendon PC Bridges is provided.

关键词： arithmetic analysis     calculation     prestressed reinforcement     mechanical     arbitrary    

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Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

《工程（英文）》 doi: 10.1016/j.eng.2023.07.018

摘要： The forward design of trajectory planning strategies requires preset trajectory optimization functions, resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits. In addition, owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios, it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters. Therefore, an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed. First, numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset. Subsequently, a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory. Furthermore, a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function, and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed. Finally, the proposed strategy is verified based on real driving scenarios. The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the “emergency degree” of obstacle avoidance and the state of the vehicle. Moreover, this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories, effectively improving the adaptability and acceptability of trajectories in driving scenarios.

关键词： Obstacle avoidance trajectory planning     Inverse reinforcement theory     Anthropomorphic     Adaptive driving scenarios    

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Automated synthesis of steady-state continuous processes using reinforcement learning

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 288-302 doi: 10.1007/s11705-021-2055-9

摘要： Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

关键词： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcement learning    

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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

《结构与土木工程前沿（英文）》 2022年 第16卷 第11期   页码 1397-1414 doi: 10.1007/s11709-022-0860-y

摘要： This paper proposes a framework for critical element identification and demolition planning of frame structures. Innovative quantitative indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learning and graph embedding. The action is defined as removing an element, and the state is described by integrating the joint and element features into a comprehensive feature vector for each element. By establishing the policy network, the agent outputs the Q value for each action after observing the state. Through numerical examples, it is confirmed that the trained agent can provide an accurate estimation of the Q values, and handle problems with different action spaces owing to utilization of graph embedding. Besides, different behaviors can be learned by varying hyperparameters in the reward function. By comparing the proposed method and the conventional sensitivity index-based methods, it is demonstrated that the computational cost is considerably reduced because the reinforcement learning model is trained offline. Besides, it is proved that the Q values produced by the reinforcement learning agent can make up for the deficiencies of existing indices, and can be directly used as the quantitative index for the decision-making for determining the most expected collapse scenario, i.e., the sequence of element removals.

关键词： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

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Effect of earth reinforcement, soil properties and wall properties on bridge MSE walls

《结构与土木工程前沿（英文）》 2021年 第15卷 第5期   页码 1209-1221 doi: 10.1007/s11709-021-0764-2

摘要： Mechanically stabilized earth (MSE) retaining walls are popular for highway bridge structures. They have precast concrete panels attached to earth reinforcement. The panels are designed to have some lateral movement. However, in some cases, excessive movement and even complete dislocation of the panels have been observed. In this study, 3-D numerical modeling involving an existing MSE wall was undertaken to investigate various wall parameters. The effects of pore pressure, soil cohesion, earth reinforcement type and length, breakage/slippage of reinforcement and concrete strength, were examined. Results showed that the wall movement is affected by soil pore pressure and reinforcement integrity and length, and unaffected by concrete strength. Soil cohesion has a minor effect, while the movement increased by 13–20 mm for flexible geogrid reinforced walls compared with the steel grid walls. The steel grid stresses were below yielding, while the geogrid experienced significant stresses without rupture. Geogrid reinforcement may be used taking account of slippage resistance and wall movement. If steel grid is used, non-cohesive soil is recommended to minimize corrosion. Proper soil drainage is important for control of pore pressure.

关键词： mechanically stabilized earth walls     precast concrete panels     backfill soil     finite element modeling     earth reinforcement    

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Layout optimization of steel reinforcement in concrete structure using a truss-continuum model

《结构与土木工程前沿（英文）》 2023年 第17卷 第5期   页码 669-685 doi: 10.1007/s11709-023-0963-0

摘要： Owing to advancement in advanced manufacturing technology, the reinforcement design of concrete structures has become an important topic in structural engineering. Based on bi-directional evolutionary structural optimization (BESO), a new approach is developed in this study to optimize the reinforcement layout in steel-reinforced concrete (SRC) structures. This approach combines a minimum compliance objective function with a hybrid truss-continuum model. Furthermore, a modified bi-directional evolutionary structural optimization (M-BESO) method is proposed to control the level of tensile stress in concrete. To fully utilize the tensile strength of steel and the compressive strength of concrete, the optimization sensitivity of steel in a concrete–steel composite is integrated with the average normal stress of a neighboring concrete. To demonstrate the effectiveness of the proposed procedures, reinforcement layout optimizations of a simply supported beam, a corbel, and a wall with a window are conducted. Clear steel trajectories of SRC structures can be obtained using both methods. The area of ​​critical tensile stress in concrete yielded by the M-BESO is more than 40% lower than that yielded by the uniform design and BESO. Hence, the M-BESO facilitates a fully digital workflow that can be extremely effective for improving the design of steel reinforcements in concrete structures.

关键词： bi-directional evolutionary structural optimization     steel-reinforced concrete     concrete stress     reinforcement method     hybrid model    

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Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

《工程（英文）》 doi: 10.1016/j.eng.2023.10.005

摘要： While autonomous vehicles are vital components of intelligent transportation systems, ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving. Therefore, we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles. The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety. Specifically, an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics. In addition, an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics. Moreover, we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety model. Finally, the proposed approach is evaluated through both simulations and experiments. These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

关键词： Autonomous vehicle     Decision-making     Reinforcement learning     Adversarial attack     Safety guarantee    

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概念加固思想及工程应用

段敬民,钱永久,张方,曾宪桃

《中国工程科学》 2005年 第7卷 第8期   页码 22-25

摘要：

提出了工程结构加固中新的思路——概念加固。概念加固是运用人的思维和判断，从宏观上决定工程加固中的基本问题、基本概念及基本原则，并阐述了基于“概念加固”思想指导下的整体预应力加固技术，介绍了在工程加固中的应用实例。

关键词： 工程结构     概念加固     整体预应力加固技术    

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Monitoring corrosion of reinforcement in concrete structures via fiber Bragg grating sensors

Zhupeng ZHENG, Xiaoning SUN, Ying LEI

《机械工程前沿（英文）》 2009年 第4卷 第3期   页码 316-319 doi: 10.1007/s11465-009-0040-y

摘要： Corrosion of steel and rebar in concrete structures is one of the most frequent reasons for civil infrastructure failures. Thus, improving the effective corrosion sensor technology can greatly reduce cost and provide safe structures with long service lives. However, assessing the corrosion condition of rebars is not simple because they are buried in concrete. In this paper, using fiber Bragg grating (FBG), a corrosion sensor for monitoring steel rebars embedded in a concrete structure is developed and validated by experiments. Based on the fact that the volume and diameter of a rebar embedded in concrete will enlarge due to corrosion, an FBG packaged with fiber-reinforced plastics (FRP) is wrapped on the steel bar. During corrosion, the increase in the bar diameter leads to the increase in fiber strain, which can be measured by the shift of the wavelength of FBG. Performances of the corrosion sensor are validated by accelerating corrosion in lab experiments. The corrosion sensor is embedded in a concrete specimen put in a 5% sodium chloride solution with a constant current. Experimental results show that the corrosion sensor can monitor the concurrence of corrosion of rebars in concrete. The corrosion extent can be quantitatively evaluated through the change in the wavelength of FBG. Therefore, the corrosion sensor developed in this paper is feasible for monitoring the early corrosion of rebars in concrete.

关键词： fiber Bragg grating (FBG)     corrosion     concrete structures     accelerated corrosion test    

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标题 作者 时间 类型 操作

Effect of axial load and transverse reinforcements on the seismic performance of reinforced concrete

Mounir Ait BELKACEM, Hakim BECHTOULA, Nouredine BOURAHLA, Adel Ait BELKACEM

期刊论文

Flexural and longitudinal shear performance of precast lightweight steel–ultra-high performance concrete composite beam

期刊论文

Detection for transverse corner cracks of steel plates’ surface using wavelet

Qiong ZHOU, Qi AN

期刊论文

Investigating the influence of delamination on the stiffness of composite pipes under compressive transverse

Sattar MALEKI, Roham RAFIEE, Abolfazl HASANNIA, Mohammad Reza HABIBAGAHI

期刊论文

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

期刊论文

Punching of reinforced concrete slab without shear reinforcement: Standard models and new proposal

Luisa PANI, Flavio STOCHINO

期刊论文

Spatial embedded reinforcement of 20-node block element for analysis PC bridges

LONG Peiheng, DU Xianting, CHEN Weizhen

期刊论文

Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

期刊论文

Automated synthesis of steady-state continuous processes using reinforcement learning

期刊论文

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

期刊论文

Effect of earth reinforcement, soil properties and wall properties on bridge MSE walls

期刊论文

Layout optimization of steel reinforcement in concrete structure using a truss-continuum model

期刊论文

Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

期刊论文

概念加固思想及工程应用

段敬民,钱永久,张方,曾宪桃

期刊论文

Monitoring corrosion of reinforcement in concrete structures via fiber Bragg grating sensors

Zhupeng ZHENG, Xiaoning SUN, Ying LEI

期刊论文