Mei qipei (11 risultati)

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Hardback. Condizione: New.

Hardback. Condizione: New.

Soft cover. Condizione: Fine. No Jacket. 1 v. (unpaged) : ill. ; 27 cm. ; Sale code: 6027 QICHANG/ Place of sale: New York ; OCLC: 477122841 ; color photographic stiff paper wrappers ; prices realized laid in ; features works by Zhao Mengfu, Jie Jisi, Ni Zan, hu Tinghui, Zheng Yuanyou, Dai Jin, Shen Zhou, Wu Wei, Tang Yin, Zhu Yunming, Wen Zhengming, Chen Shun, Qiu Ying, Qian Gu, Yu Yuanlun, Fan Dongming, Dong Qichang, Chen Jiru, Qian Gong, Huang Biao, Guan Si, Zhang Ruitu, Wang Duo, Lan Ying, Huang Xiangjian, Wang Jian, Xie Bin, Gao Cen, Li Yin, Mei Qing, Da Chongguang, Lai Jing, Zha Shibiao, Sheng Maojun, Zhang Qi, Wu Hong, Ma Quan, Qin Ji, Ye Xin, Fu Shan, Huang Daoqian, Jiang Shijie, Luo Mu, Fa Rouzhen, Wang Hui, Ma Yuanyu, Li Shan, Jiao Bingzhen, Wang Gang, ao Qipei, Jiang Tingxi, Hua Yan, Jin Nong, Zhang Ruoai, Zheng Xie, Huang Shen, Zhang Ruocheng, Dong Bangda, Wang Chen, Qian Feng, Yuan Ying, Fang Xun, Luo Ping, Pan Gongshou, Jiang Wenzai, Sun Shigao, Xi Gang, Tie Bao, Yuan Pei, Zhai Jichang, Weng Luo, Zhu Haonian, Shen Rong, Tang Yifen, Mao Zhou, Qu Yingshao, Su Renshan, Dai Xi, He Shaoji, Su Liupeng, Peng Yulin, Ren Yi, Wu Changshuo, Gu Linshi, Zeng Xi, Qi Baishi, Pu Ru, Pu Jin, Fu Baoshi, Feng Zhikai, Ding Yanyon, Zhang DaiqianLin FengmianWang Kangle, He Haixia, Xu Zhe, Li Xiongcai, Wu Guanzhong, Zhang Shoucheng, Cheng Shifa ; FINE. Book.…

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Hardcover. Condizione: new. Hardcover. This Reprint presents a comprehensive collection of cutting-edge research on structural identification and damage evaluation through the integration of physics-based models with data-driven approaches. The compilation addresses one of the most critical challenges in structural health monitoring: combining the theoretical rigor of physics-based numerical models with the adaptive capabilities of modern data science techniques.The featured studies demonstrate innovative methodologies that bridge traditional finite element model updating approaches with advanced machine learning algorithms, physics-informed neural networks, and Bayesian inference techniques. Researchers explore novel applications including deep learning-enhanced stress identification in prestressed structures, automated concrete crack detection using computer vision, and real-time structural assessment through digital twin technologies.Key contributions encompass deterministic and stochastic finite element model updating, physics-guided machine learning for damage detection, hybrid modeling frameworks for structural systems, and uncertainty quantification in structural assessment. The Reprint showcases practical implementations across diverse structural types, from high-rise buildings and bridge systems to specialized infrastructure components like lightning rod structures and prestressed concrete girders. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This Reprint presents a comprehensive collection of cutting-edge research on structural identification and damage evaluation through the integration of physics-based models with data-driven approaches. The compilation addresses one of the most critical challenges in structural health monitoring: combining the theoretical rigor of physics-based numerical models with the adaptive capabilities of modern data science techniques.The featured studies demonstrate innovative methodologies that bridge traditional finite element model updating approaches with advanced machine learning algorithms, physics-informed neural networks, and Bayesian inference techniques. Researchers explore novel applications including deep learning-enhanced stress identification in prestressed structures, automated concrete crack detection using computer vision, and real-time structural assessment through digital twin technologies.Key contributions encompass deterministic and stochastic finite element model updating, physics-guided machine learning for damage detection, hybrid modeling frameworks for structural systems, and uncertainty quantification in structural assessment. The Reprint showcases practical implementations across diverse structural types, from high-rise buildings and bridge systems to specialized infrastructure components like lightning rod structures and prestressed concrete girders.…