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分组数据处理与遗传算法 1

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Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

《结构与土木工程前沿（英文）》 2022年第16卷第2期页码 250-265 doi: 10.1007/s11709-021-0785-x

摘要： In this study, the relationship between space mean speed (SMS), flow rate and density of pedestrians was investigated in different pedestrian facilities, including 1 walkway, 2 sidewalks, 2 signalized crosswalks and 2 mid-block crosswalks. First, statistical analysis was performed to investigate the normality of data and correlation of variables. Regression analysis was then applied to determine the relationship between SMS, flow rate, and density of pedestrians. Finally, two prediction models of density were obtained using genetic programming (GP) and group method of data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluate the models. By the use of regression analysis, the mathematical relationships between variables in all facilities were calculated and plotted, and the best relationships were observed in flow rate-density diagrams. Results also indicated that GP had a higher R² than GMDH in the prediction of pedestrian density in terms of flow rate and SMS, suggesting that GP was better able to model SMS and pedestrian density. Moreover, the application of k-fold cross-validation method in the models led to better performances compared to the holdout cross-validation method, which shows that the prediction models using k-fold were more reliable. Finally, density relationships in all facilities were obtained in terms of SMS and flow rate.

关键词： pedestrian density regression analysis GP model GMDH model

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遗传算法与分组数据处理神经网络相结合的人工智能预测盾构掘进过程中滚刀的寿命 Article

Khalid Elbaz, 沈水龙, 周安楠, 尹振宇, 吕海敏

《工程（英文）》 2021年第7卷第2期页码 238-251 doi: 10.1016/j.eng.2020.02.016

摘要：本研究提出了一种估算滚刀寿命（H_f）的新模型，模型将分组数据处理（GMDH）型神经网络（NN）与遗传算法（GA）整合在一起。遗传算法优化了GMDH网络结构的效率和有效性，使得每个神经元都能从上一层网络搜索最佳连接集。使用所提出的模型，可以分析盾构机性能数据库、滚刀的消耗、地质条件和操作参数等监测数据。

关键词：滚刀寿命盾构隧道施工参数分组数据处理与遗传算法

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An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model

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