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Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristicalgorithms

《结构与土木工程前沿（英文）》 2022年 第16卷 第1期   页码 57-74 doi: 10.1007/s11709-021-0784-y

摘要： Steel dome structures, with their striking structural forms, take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns. In this paper, the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider (WS), grey wolf (GW), and brain storm optimization (BSO). The structural elements of the domes are treated as design variables collected in member groups. The structural stress and stability limitations are enforced by ASD-AISC provisions. Also, the displacement restrictions are considered in design procedure. The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface (OAPI). The optimum spatial steel dome designs achieved by proposed WS, GW, and BSO algorithms are compared with respect to solution accuracy, convergence rates, and reliability, utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization (HBO) algorithm.

关键词： steel dome optimization     water strider algorithm     grey wolf algorithm     brain storm optimization algorithm     hyperband optimization algorithm    

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Estimation of optimum design of structural systems via machine learning

《结构与土木工程前沿（英文）》 2021年 第15卷 第6期   页码 1441-1452 doi: 10.1007/s11709-021-0774-0

摘要： Three different structural engineering designs were investigated to determine optimum design variables, and then to estimate design parameters and the main objective function of designs directly, speedily, and effectively. Two different optimization operations were carried out: One used the harmony search (HS) algorithm, combining different ranges of both HS parameters and iteration with population numbers. The other used an estimation application that was done via artificial neural networks (ANN) to find out the estimated values of parameters. To explore the estimation success of ANN models, different test cases were proposed for the three structural designs. Outcomes of the study suggest that ANN estimation for structures is an effective, successful, and speedy tool to forecast and determine the real optimum results for any design model.

关键词： optimization     metaheuristic algorithms     harmony search     structural designs     machine learning     artificial neural networks    

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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

《结构与土木工程前沿（英文）》   页码 812-826 doi: 10.1007/s11709-023-0940-7

摘要： A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer     modulus of subgrade reaction     elastic modulus     metaheuristic algorithms    

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Analytical algorithms of compressive bending capacity of bolted circumferential joint in metro shield

《结构与土木工程前沿（英文）》   页码 901-914 doi: 10.1007/s11709-023-0915-8

摘要： The integrity and bearing capacity of segment joints in shield tunnels are associated closely with the mechanical properties of the joints. This study focuses on the mechanical characteristics and mechanism of a bolted circumferential joint during the entire bearing process. Simplified analytical algorithms for four stress stages are established to describe the bearing behaviors of the joint under a compressive bending load. A height adjustment coefficient, α, for the outer concrete compression zone is introduced into a simplified analytical model. Factors affecting α are determined, and the degree of influence of these factors is investigated via orthogonal numerical simulations. The numerical results show that α can be specified as approximately 0.2 for most metro shield tunnels in China. Subsequently, a case study is performed to verify the rationality of the simplified theoretical analysis for the segment joint via numerical simulations and experiments. Using the proposed simplified analytical algorithms, a parametric investigation is conducted to discuss the factors affecting the ultimate compressive bending capacity of the joint. The method for optimizing the joint flexural stiffness is clarified. The results of this study can provide a theoretical basis for optimizing the design and prediciting the damage of bolted segment joints in shield tunnels.

关键词： shield tunnel     segment joint     joint structural model     failure mechanism    

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Crack detection of the cantilever beam using new triple hybrid algorithms based on Particle Swarm Optimization

Amin GHANNADIASL; Saeedeh GHAEMIFARD

《结构与土木工程前沿（英文）》 2022年 第16卷 第9期   页码 1127-1140 doi: 10.1007/s11709-022-0838-9

摘要： The presence of cracks in a concrete structure reduces its performance and increases in the size of cracks result in the failure of the structure. Therefore, the accurate determination of crack characteristics, such as location and depth, is one of the key engineering issues for assessment of the reliability of structures. This paper deals with the inverse analysis of the crack detection problems using triple hybrid algorithms based on Particle Swarm Optimization (PSO); these hybrids are Particle Swarm Optimization-Genetic Algorithm-Firefly Algorithm (PSO-GA-FA), Particle Swarm Optimization-Grey Wolf Optimization-Firefly Algorithm (PSO-GWO-FA), and Particle Swarm Optimization-Genetic Algorithm-Grey Wolf Optimization (PSO-GA-GWO). A strong correlation exists between the changes in the natural frequency of a concrete beam and the crack parameters. Thus, the location and depth of a crack in a beam can be predicted by measuring its natural frequency. Hence, the measured natural frequency can be used as the input parameter of the algorithm. In this paper, this is applied to identify crack location and depth in a cantilever beam using the new hybrid algorithms. The results show that among the proposed triple hybrid algorithms, the PSO-GA-FA and PSO-GWO-FA algorithms are much more effective than PSO-GA-GWO algorithm for the crack detection.

关键词： crack     cantilever beam     triple hybrid algorithms     Particle Swarm Optimization    

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Multi-objective genetic algorithms based structural optimization and experimental investigation of the

Pengxing YI,Lijian DONG,Tielin SHI

《机械工程前沿（英文）》 2014年 第9卷 第4期   页码 354-367 doi: 10.1007/s11465-014-0319-5

摘要：

To improve the dynamic performance and reduce the weight of the planet carrier in wind turbine gearbox, a multi-objective optimization method, which is driven by the maximum deformation, the maximum stress and the minimum mass of the studied part, is proposed by combining the response surface method and genetic algorithms in this paper. Firstly, the design points’ distribution for the design variables of the planet carrier is established with the central composite design (CCD) method. Then, based on the computing results of finite element analysis (FEA), the response surface analysis is conducted to find out the proper sets of design variable values. And a multi-objective genetic algorithm (MOGA) is applied to determine the direction of optimization. As well, this method is applied to design and optimize the planet carrier in a 1.5 MW wind turbine gearbox, the results of which are validated by an experimental modal test. Compared with the original design, the mass and the stress of the optimized planet carrier are respectively reduced by 9.3% and 40%. Consequently, the cost of planet carrier is greatly reduced and its stability is also improved.

关键词： planet carrier     multi-objective optimization     genetic algorithms     wind turbine gearbox     modal experiment    

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Development of surface reconstruction algorithms for optical interferometric measurement

Dongxu WU, Fengzhou FANG

《机械工程前沿（英文）》 2021年 第16卷 第1期   页码 1-31 doi: 10.1007/s11465-020-0602-6

摘要： Optical interferometry is a powerful tool for measuring and characterizing areal surface topography in precision manufacturing. A variety of instruments based on optical interferometry have been developed to meet the measurement needs in various applications, but the existing techniques are simply not enough to meet the ever-increasing requirements in terms of accuracy, speed, robustness, and dynamic range, especially in on-line or on-machine conditions. This paper provides an in-depth perspective of surface topography reconstruction for optical interferometric measurements. Principles, configurations, and applications of typical optical interferometers with different capabilities and limitations are presented. Theoretical background and recent advances of fringe analysis algorithms, including coherence peak sensing and phase-shifting algorithm, are summarized. The new developments in measurement accuracy and repeatability, noise resistance, self-calibration ability, and computational efficiency are discussed. This paper also presents the new challenges that optical interferometry techniques are facing in surface topography measurement. To address these challenges, advanced techniques in image stitching, on-machine measurement, intelligent sampling, parallel computing, and deep learning are explored to improve the functional performance of optical interferometry in future manufacturing metrology.

关键词： surface topography     measurement     optical interferometry     coherence envelope     phase-shifting algorithm    

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Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive

Dung Quang VU; Fazal E. JALAL; Mudassir IQBAL; Dam Duc NGUYEN; Duong Kien TRONG; Indra PRAKASH; Binh Thai PHAM

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 1003-1016 doi: 10.1007/s11709-022-0846-9

摘要： In this study, we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System (ANFIS) optimized by Shuffled Complex Evolution (SCE) on the one hand and ANFIS with Artificial Bee Colony (ABC) on the other hand. These were used to predict compressive strength (Cs) of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory. Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway, Vietnam were considered. The dataset was randomly divided into a 70:30 ratio, for training (70%) and testing (30%) of the hybrid models. Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics: Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that both of the novel models depict close agreement between experimental and predicted results. However, the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs. Thus, the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.

关键词： shuffled complex evolution     artificial bee colony     ANFIS     concrete     compressive strength     Vietnam    

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关联规则挖掘算法综述

毕建欣,张岐山

《中国工程科学》 2005年 第7卷 第4期   页码 88-94

摘要：

介绍了关联规则挖掘算法的基本原理，并按照挖掘中涉及到的变量数目（维数）、数据的抽象层次和处理变量的类别（布尔型和数值型），依次对关联规则挖掘算法的研究进行综述，并对一些典型的算法进行分析和比较，最后展望了关联规则挖掘算法的研究方向。

关键词： 数据挖掘     关联规则     算法     综述    

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多目标优化与决策问题的演化算法

谢涛,陈火旺

《中国工程科学》 2002年 第4卷 第2期   页码 59-68

摘要：

近年来，多目标优化与决策问题求解已成为演化计算的一个重要研究方向。为使演化算法的种群解 能尽快收敛并均匀分布于多目标问题的非劣最优域，多目标演化算法的研究热点集中在基于Pareto最优概念的 种群个体的比较与排序、适应值賦值与小生境技术等方面。介绍了多目标优化与决策技术的发展历史与分类方 法，分析了基于Pareto最优概念与不基于Pareto最优概念两大类的多目标演化算法，并详细比较与分析了几种 典型多目标演化算法。其次，论述了与多目标演化算法研究紧密相关的一些问题，如多目标问题解的性质，测 试函数集设计，算法性能评估技术，算法收敛性，并行实现以及实际多目标优化问题的处理等。

关键词： 演化计算     多目标优化与决策     Pareto最优    

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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 490-505 doi: 10.1007/s11709-020-0669-5

摘要： This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy ( ), precision, recall, , and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

关键词： seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structural learning    

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An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

《结构与土木工程前沿（英文）》 2020年 第14卷 第4期   页码 907-929 doi: 10.1007/s11709-020-0628-1

摘要： In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation ( ) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

关键词： two-stage method     modal strain energy     surrogate model     GMDH     optimization damage detection    

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Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized

《结构与土木工程前沿（英文）》 2023年 第17卷 第3期   页码 378-395 doi: 10.1007/s11709-022-0899-9

摘要： This paper presents a combined method based on optimized neural networks and optimization algorithms to solve structural optimization problems. The main idea is to utilize an optimized artificial neural network (OANN) as a surrogate model to reduce the number of computations for structural analysis. First, the OANN is trained appropriately. Subsequently, the main optimization problem is solved using the OANN and a population-based algorithm. The algorithms considered in this step are the arithmetic optimization algorithm (AOA) and genetic algorithm (GA). Finally, the abovementioned problem is solved using the optimal point obtained from the previous step and the pattern search (PS) algorithm. To evaluate the performance of the proposed method, two numerical examples are considered. In the first example, the performance of two algorithms, OANN + AOA + PS and OANN + GA + PS, is investigated. Using the GA reduces the elapsed time by approximately 50% compared with using the AOA. Results show that both the OANN + GA + PS and OANN + AOA + PS algorithms perform well in solving structural optimization problems and achieve the same optimal design. However, the OANN + GA + PS algorithm requires significantly fewer function evaluations to achieve the same accuracy as the OANN + AOA + PS algorithm.

关键词： optimization     surrogate models     artificial neural network     SAP2000     genetic algorithm    

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基于遗传算法的火灾环境下建筑内人员安全疏散可靠度计算

汪金辉,陆守香

《中国工程科学》 2006年 第8卷 第3期   页码 58-61

摘要：

将遗传算法（GAs）引入火灾环境下建筑内人员安全疏散的可靠度分析领域，并初步探讨了这一算法的具体实现过程，遗传算法可以方便地对式子复杂和难以求导的功能函数进行优化计算。算例分析的结果表明，遗传算法不仅可以实现人员安全疏散可靠度计算，而且获得了很高的计算精度，为人员安全疏散可靠度研究提供了新的有效方法和途径。

关键词： 遗传算法     火灾     人员疏散     可靠度    

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Review of self-referenced measurement algorithms: Bridging lateral shearing interferometry and multi-probe

Dede ZHAI, Shanyong CHEN, Ziqiang YIN, Shengyi LI

《机械工程前沿（英文）》 2017年 第12卷 第2期   页码 143-157 doi: 10.1007/s11465-017-0432-3

摘要：

With the development of new materials and ultra-precision processing technology, the sizes of measured objects increase, and the requirements for machining accuracy and surface quality become more exacting. The traditional measurement method based on reference datum is inadequate for measuring a high-precision object when the quality of the reference datum is approximately within the same order as that of the object. Self-referenced measurement techniques provide an effective means when the direct reference-based method cannot satisfy the required measurement or calibration accuracy. This paper discusses the reconstruction algorithms for self-referenced measurement and connects lateral shearing interferometry and multi-probe error separation. In lateral shearing interferometry, the reconstruction algorithms are generally categorized into modal or zonal methods. The multi-probe error separation techniques for straightness measurement are broadly divided into two-point and three-point methods. The common features of the lateral shearing interferometry method and the multi-probe error separation method are identified. We conclude that the reconstruction principle in lateral shearing interferometry is similar to the two-point method in error separation on the condition that no yaw error exists. This similarity may provide a basis or inspiration for the development of both classes of methods.

关键词： self-referenced measurement     lateral shearing interferometry     multi-probe error separation     surface metrology    

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标题 作者 时间 类型 操作

Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristicalgorithms

期刊论文

Estimation of optimum design of structural systems via machine learning

期刊论文

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

期刊论文

Analytical algorithms of compressive bending capacity of bolted circumferential joint in metro shield

期刊论文

Crack detection of the cantilever beam using new triple hybrid algorithms based on Particle Swarm Optimization

Amin GHANNADIASL; Saeedeh GHAEMIFARD

期刊论文

Multi-objective genetic algorithms based structural optimization and experimental investigation of the

Pengxing YI,Lijian DONG,Tielin SHI

期刊论文

Development of surface reconstruction algorithms for optical interferometric measurement

Dongxu WU, Fengzhou FANG

期刊论文

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive

Dung Quang VU; Fazal E. JALAL; Mudassir IQBAL; Dam Duc NGUYEN; Duong Kien TRONG; Indra PRAKASH; Binh Thai PHAM

期刊论文

关联规则挖掘算法综述

毕建欣,张岐山

期刊论文

多目标优化与决策问题的演化算法

谢涛,陈火旺

期刊论文

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

期刊论文

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

期刊论文

Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized

期刊论文

基于遗传算法的火灾环境下建筑内人员安全疏散可靠度计算

汪金辉,陆守香

期刊论文

Review of self-referenced measurement algorithms: Bridging lateral shearing interferometry and multi-probe

Dede ZHAI, Shanyong CHEN, Ziqiang YIN, Shengyi LI

期刊论文