A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized fle...A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.展开更多
Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvat...Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvatures of mode shapes of the periodic spring-mass system by utilizing the periodic structure theory are derived in this paper. The sensitivities of these mode parameters with respect to structural damages, which do not depend on the physical parameters of the original structures, are obtained. Based on the sensitivity analysis of these mode parameters, a two-stage method is proposed to localize and quantify damages of multi-story or highrise buildings. The slopes and curvatures of mode shapes, which are highly sensitive to local damages, are used to localize the damages. Subsequently, the limited measured natural frequencies, which have a better accuracy than the other mode parameters, are used to quantify the extent of damages within the potential damaged locations. The experimental results of a 3-story experimental building demonstrate that the single or multiple damages of buildings, either slight or severe, can be correctly localized by using only the slope or curvature of mode shape in one of the lower modes, in which the change of natural frequency is the largest, and can be accurately quantified by the limited measured natural frequencies with noise pollution.展开更多
桥梁健康监测数据的挖掘和分析工作只有在整体数据质量符合基本要求的有效数据基础上进行,才能保障如模态参数识别、损伤识别和状态评估等后续工作的准确性。因此,基于量化改进的探索性分析方法(Exploratory Data Analysis,EDA)和相关...桥梁健康监测数据的挖掘和分析工作只有在整体数据质量符合基本要求的有效数据基础上进行,才能保障如模态参数识别、损伤识别和状态评估等后续工作的准确性。因此,基于量化改进的探索性分析方法(Exploratory Data Analysis,EDA)和相关性分析从数据完整性、准确性和一致性的角度建立了桥梁健康监测静、动态数据的质量评估方法。对某大跨度斜拉桥健康监测系统的静、动态数据进行质量评估,通过对比分析了不同评估质量的温度数据、静挠度数据和不同评估质量的主梁竖向加速度动力信号的模态参数识别的稳定图,验证了所提方法的正确性。结果表明,所提评估方法能够快速有效地判断数据质量的好坏,进而确保桥梁结构的服役性能评估和预测的准确性,有利于提高健康监测数据的可用性和效能。展开更多
针对“校园大数据”累积的海量数据呈现出离散性、稀疏性等问题,如何从基数大、活动广、个性强的校园学生群体中检测出潜在的、有异常行为的学生,已成为学生异常行为分析亟需解决的问题.本文提出了一种大数据环境下基于多模态融合的大...针对“校园大数据”累积的海量数据呈现出离散性、稀疏性等问题,如何从基数大、活动广、个性强的校园学生群体中检测出潜在的、有异常行为的学生,已成为学生异常行为分析亟需解决的问题.本文提出了一种大数据环境下基于多模态融合的大学生异常行为预警方法(early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment,EWMAB).首先,针对学生行为画像的表征不够丰富,行为标签存在时效性、动态性等问题,建立一种基于多模态特征深度学习的跨模态学生行为画像模型;其次,针对学生异常行为预测、预警的时效性和后置性问题,在学生行为画像和学生行为分类预测基础上,提出了一种基于多模态融合的学生异常行为预警方法,通过长短期记忆神经网络(long and short term memory networks,LSTM),结合学生行为多指标数据和文本信息来解决学生异常行为预警问题;最后,本文通过应用实例验证模型以学生学习成绩异常预警为例,与其他预警算法相比,EWMAB方法可以提高预警的准确性,实现学生异常行为预警的时效性和前置性,从而使学生教育工作更具有针对性、个性化和预测性.展开更多
【目的】基于社交媒体数据的公园研究已成为热点。然而,既有研究依赖单模态数据和自然语言处理(natural language processing,NLP)技术,研究结果的精确度有待提升。随着大语言模型(large language models,LLM)的发展,分析社交媒体数据...【目的】基于社交媒体数据的公园研究已成为热点。然而,既有研究依赖单模态数据和自然语言处理(natural language processing,NLP)技术,研究结果的精确度有待提升。随着大语言模型(large language models,LLM)的发展,分析社交媒体数据可实现更精确的城市公园公众活动丰富度解析。【方法】先利用LLM解析包含文本、图像和视频的多模态社交媒体数据,再运用聚类算法探究用户的情感倾向和活动丰富度,生成活动热力图,构建公园公众活动丰富度的量化方法。【结果】以传统问卷方法为参照标准,对比分析发现基于多模态数据的LLM分析法的准确性远优于单模态数据分析法,证实了研究方法的有效性。并将LLM分析法应用于上海外环内的20个城市公园,构建出大规模、高精度的公园公众活动丰富度的全景测度方法。【结论】创新性地利用LLM和多模态社交媒体数据分析城市公园公众活动丰富度,有利于推动人工智能在城市研究领域的学术发展和应用。展开更多
文摘A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.
基金Project supported by the National Natural Science Foundation of China (No. 50378041) Specialized Research Fund for Doctoral Programs of Higher Education (No. 20030487016).
文摘Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvatures of mode shapes of the periodic spring-mass system by utilizing the periodic structure theory are derived in this paper. The sensitivities of these mode parameters with respect to structural damages, which do not depend on the physical parameters of the original structures, are obtained. Based on the sensitivity analysis of these mode parameters, a two-stage method is proposed to localize and quantify damages of multi-story or highrise buildings. The slopes and curvatures of mode shapes, which are highly sensitive to local damages, are used to localize the damages. Subsequently, the limited measured natural frequencies, which have a better accuracy than the other mode parameters, are used to quantify the extent of damages within the potential damaged locations. The experimental results of a 3-story experimental building demonstrate that the single or multiple damages of buildings, either slight or severe, can be correctly localized by using only the slope or curvature of mode shape in one of the lower modes, in which the change of natural frequency is the largest, and can be accurately quantified by the limited measured natural frequencies with noise pollution.
文摘桥梁健康监测数据的挖掘和分析工作只有在整体数据质量符合基本要求的有效数据基础上进行,才能保障如模态参数识别、损伤识别和状态评估等后续工作的准确性。因此,基于量化改进的探索性分析方法(Exploratory Data Analysis,EDA)和相关性分析从数据完整性、准确性和一致性的角度建立了桥梁健康监测静、动态数据的质量评估方法。对某大跨度斜拉桥健康监测系统的静、动态数据进行质量评估,通过对比分析了不同评估质量的温度数据、静挠度数据和不同评估质量的主梁竖向加速度动力信号的模态参数识别的稳定图,验证了所提方法的正确性。结果表明,所提评估方法能够快速有效地判断数据质量的好坏,进而确保桥梁结构的服役性能评估和预测的准确性,有利于提高健康监测数据的可用性和效能。
文摘针对“校园大数据”累积的海量数据呈现出离散性、稀疏性等问题,如何从基数大、活动广、个性强的校园学生群体中检测出潜在的、有异常行为的学生,已成为学生异常行为分析亟需解决的问题.本文提出了一种大数据环境下基于多模态融合的大学生异常行为预警方法(early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment,EWMAB).首先,针对学生行为画像的表征不够丰富,行为标签存在时效性、动态性等问题,建立一种基于多模态特征深度学习的跨模态学生行为画像模型;其次,针对学生异常行为预测、预警的时效性和后置性问题,在学生行为画像和学生行为分类预测基础上,提出了一种基于多模态融合的学生异常行为预警方法,通过长短期记忆神经网络(long and short term memory networks,LSTM),结合学生行为多指标数据和文本信息来解决学生异常行为预警问题;最后,本文通过应用实例验证模型以学生学习成绩异常预警为例,与其他预警算法相比,EWMAB方法可以提高预警的准确性,实现学生异常行为预警的时效性和前置性,从而使学生教育工作更具有针对性、个性化和预测性.
文摘【目的】基于社交媒体数据的公园研究已成为热点。然而,既有研究依赖单模态数据和自然语言处理(natural language processing,NLP)技术,研究结果的精确度有待提升。随着大语言模型(large language models,LLM)的发展,分析社交媒体数据可实现更精确的城市公园公众活动丰富度解析。【方法】先利用LLM解析包含文本、图像和视频的多模态社交媒体数据,再运用聚类算法探究用户的情感倾向和活动丰富度,生成活动热力图,构建公园公众活动丰富度的量化方法。【结果】以传统问卷方法为参照标准,对比分析发现基于多模态数据的LLM分析法的准确性远优于单模态数据分析法,证实了研究方法的有效性。并将LLM分析法应用于上海外环内的20个城市公园,构建出大规模、高精度的公园公众活动丰富度的全景测度方法。【结论】创新性地利用LLM和多模态社交媒体数据分析城市公园公众活动丰富度,有利于推动人工智能在城市研究领域的学术发展和应用。
