F. Nicassio, Pierandrea Vergallo, R. Vitolo, G. Scarselli
A finite difference algorithm that evaluates the health conditions of a bonded joint is presented and discussed. The mathematical formulation of the problem is developed, paying particular attention to the singularity around the corners of the joint and implementing an original discretisation method of the partial differential equations governing the propagation of the elastic waves. The equations are solved under the only hypothesis of a bidimensional field. The algorithm is sensible to defects in the bonded joint and can be used as an effective structural health monitoring tool, as proven by the experiments that show close agreement with the numerical simulations.
{"title":"Two Dimensional Finite Difference Model with a Singularity Attenuation Factor for Structural Health Monitoring of Single Lap Joints","authors":"F. Nicassio, Pierandrea Vergallo, R. Vitolo, G. Scarselli","doi":"10.1155/2023/1429761","DOIUrl":"https://doi.org/10.1155/2023/1429761","url":null,"abstract":"A finite difference algorithm that evaluates the health conditions of a bonded joint is presented and discussed. The mathematical formulation of the problem is developed, paying particular attention to the singularity around the corners of the joint and implementing an original discretisation method of the partial differential equations governing the propagation of the elastic waves. The equations are solved under the only hypothesis of a bidimensional field. The algorithm is sensible to defects in the bonded joint and can be used as an effective structural health monitoring tool, as proven by the experiments that show close agreement with the numerical simulations.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84230833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kou Miyamoto, Jun Iba, Koichi Watanabe, K. Ishii, M. Kikuchi
Seismic isolation is widely used in several countries, and the number of seismically isolated buildings has increased rapidly in recent decades. Seismic isolation extends the natural period of a building to decrease the absolute acceleration and seismic force. As there is a trade-off between the absolute acceleration and displacement, a soft layer results in a large displacement for a large seismic wave, but the hard one causes the large absolute acceleration even for a small seismic wave. The restoring force of a Duffing spring is given by the third and first orders of the displacement. This spring has been applied to protect a building from large earthquake waves. However, the influence of the coefficient of the Duffing spring that determines the dynamic characteristics of the system has not been clarified. Thus, a used Duffing spring may not be appropriate for seismic resistance. Moreover, most studies are based on analytical methods, and the advantages of the Duffing isolation have not been verified in an actual system. To address these problems, this paper reveals the influence of the coefficient of the Duffing spring on structural responses to seismic waves. Moreover, this paper devised a way to implement a Duffing spring for seismic isolation and carried out experiments to verify the validity in actual systems. The experimental results presented that the Duffing spring was effective in protecting a building in actual systems.
{"title":"Development of Nonlinear Geometric Seismic Isolation with a Duffing Spring","authors":"Kou Miyamoto, Jun Iba, Koichi Watanabe, K. Ishii, M. Kikuchi","doi":"10.1155/2023/3917013","DOIUrl":"https://doi.org/10.1155/2023/3917013","url":null,"abstract":"Seismic isolation is widely used in several countries, and the number of seismically isolated buildings has increased rapidly in recent decades. Seismic isolation extends the natural period of a building to decrease the absolute acceleration and seismic force. As there is a trade-off between the absolute acceleration and displacement, a soft layer results in a large displacement for a large seismic wave, but the hard one causes the large absolute acceleration even for a small seismic wave. The restoring force of a Duffing spring is given by the third and first orders of the displacement. This spring has been applied to protect a building from large earthquake waves. However, the influence of the coefficient of the Duffing spring that determines the dynamic characteristics of the system has not been clarified. Thus, a used Duffing spring may not be appropriate for seismic resistance. Moreover, most studies are based on analytical methods, and the advantages of the Duffing isolation have not been verified in an actual system. To address these problems, this paper reveals the influence of the coefficient of the Duffing spring on structural responses to seismic waves. Moreover, this paper devised a way to implement a Duffing spring for seismic isolation and carried out experiments to verify the validity in actual systems. The experimental results presented that the Duffing spring was effective in protecting a building in actual systems.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74544034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, the strain measurement accuracy of single-mode fibre (SMF) under thermal and vibration loads is investigated by strain-frequency shift coefficient analyses. This research allows for the application of SMF sensors for structural health monitoring in real operational conditions. The strain measurement accuracy under combined static and thermal load is investigated experimentally, which demonstrated that temperature fluctuations induce non-negligible errors in the strain measurement, even with temperature compensation applied. The temperature fluctuation range which can induce measurement errors is quantified as less than −20 ° C or higher than 55 ° C. In addition, a fatigue experiment is conducted to investigate the measurement accuracy under low-frequency vibration load. The results of the fatigue experiment demonstrate that the vibrations mainly increase the ratio of null values in strain measurements. Findings from experiments can be applied to enhance structural health monitoring accuracy and reduce false positives. This study has important implications for the service application of distributed optical fibre sensing for composite structure health monitoring.
{"title":"Accuracy of Distributed Strain Sensing with Single-Mode Fibre in Composite Laminates under Thermal and Vibration Loads","authors":"Yingwu Li, Z. Sharif-Khodaei","doi":"10.1155/2023/9269987","DOIUrl":"https://doi.org/10.1155/2023/9269987","url":null,"abstract":"In this work, the strain measurement accuracy of single-mode fibre (SMF) under thermal and vibration loads is investigated by strain-frequency shift coefficient analyses. This research allows for the application of SMF sensors for structural health monitoring in real operational conditions. The strain measurement accuracy under combined static and thermal load is investigated experimentally, which demonstrated that temperature fluctuations induce non-negligible errors in the strain measurement, even with temperature compensation applied. The temperature fluctuation range which can induce measurement errors is quantified as less than −20\u0000 \u0000 °\u0000 \u0000 C or higher than 55\u0000 \u0000 °\u0000 \u0000 C. In addition, a fatigue experiment is conducted to investigate the measurement accuracy under low-frequency vibration load. The results of the fatigue experiment demonstrate that the vibrations mainly increase the ratio of null values in strain measurements. Findings from experiments can be applied to enhance structural health monitoring accuracy and reduce false positives. This study has important implications for the service application of distributed optical fibre sensing for composite structure health monitoring.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82041763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a two-step vibration-based strategy for damage identification of framed structures using ensemble bagged trees known as a well-known supervised machine learning (ML) paradigm in conjunction with evolutionary optimization algorithms. The proposed model incorporates the actual response, wavelet coefficients, and wavelet energy to extract damage-sensitive features from the time-domain of the measured and simulated signals. Unlike available studies in this scope, the key objective of this research is to identify damage with a localization precision down to a single structural member, rather than limiting the evaluation to the group of elements. In order to increase the training performance in contributing to extremely large datasets with numerous class labels, the proposed strategy involves the artificial generation of features. Additionally, a modified genetic algorithm is proposed for fast damage localization. It is deduced that the damage locations are confidently detected within a fast computational time. Subsequently, damage identification is followed by the application of evolutionary optimization algorithms. For comparison purpose, the employment of the water cycle optimization algorithm (WCA) is comparatively investigated with the other three state-of-the-art optimizers, i.e., particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and differential evolution algorithm (DE). The numerical and experimental validation studies evidence satisfactorily reliable identification results with no false detection in dealing with multiple damage scenarios in large-scale and real-world applications. It is concluded that developing the most damage-sensitive features and using the proposed data fusion strategy lead to informative features with a reasonably small size and significantly improve the ML performance.
{"title":"Time-Domain Structural Damage Identification Using Ensemble Bagged Trees and Evolutionary Optimization Algorithms","authors":"S. H. Mahdavi, Chao Xu","doi":"10.1155/2023/6321012","DOIUrl":"https://doi.org/10.1155/2023/6321012","url":null,"abstract":"This paper presents a two-step vibration-based strategy for damage identification of framed structures using ensemble bagged trees known as a well-known supervised machine learning (ML) paradigm in conjunction with evolutionary optimization algorithms. The proposed model incorporates the actual response, wavelet coefficients, and wavelet energy to extract damage-sensitive features from the time-domain of the measured and simulated signals. Unlike available studies in this scope, the key objective of this research is to identify damage with a localization precision down to a single structural member, rather than limiting the evaluation to the group of elements. In order to increase the training performance in contributing to extremely large datasets with numerous class labels, the proposed strategy involves the artificial generation of features. Additionally, a modified genetic algorithm is proposed for fast damage localization. It is deduced that the damage locations are confidently detected within a fast computational time. Subsequently, damage identification is followed by the application of evolutionary optimization algorithms. For comparison purpose, the employment of the water cycle optimization algorithm (WCA) is comparatively investigated with the other three state-of-the-art optimizers, i.e., particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and differential evolution algorithm (DE). The numerical and experimental validation studies evidence satisfactorily reliable identification results with no false detection in dealing with multiple damage scenarios in large-scale and real-world applications. It is concluded that developing the most damage-sensitive features and using the proposed data fusion strategy lead to informative features with a reasonably small size and significantly improve the ML performance.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82299647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ruihua Liang, Weifeng Liu, S. Kaewunruen, Hougui Zhang, Zongzhen Wu
Excessive external vibrations could affect the normal functioning and integrity of sensitive buildings such as laboratories and heritage buildings. Usually, these buildings are exposed to multiple external vibration sources simultaneously, so the monitoring and respective evaluation of the vibration from various sources is necessary for the design of targeted vibration mitigation measures. To classify the sources of vibration accurately and efficiently, the advanced hybrid models of the convolutional neural network (CNN) and long short-term memory (LSTM) network were built in this study, and the models are driven by the extensive data of external vibration recorded in Beijing, and the parametric studies reveal that the proposed optimal model can achieve an accuracy of over 97% for the identification of external vibration sources. Finally, a real-world case study is presented, in which external vibration monitoring was carried out in a laboratory and the proposed CNN+LSTM model was used to identify the sources of vibration in the monitoring so that the impact of vibration from each source on the laboratory was analyzed statistically in detail. The results demonstrate the necessity of this study and its feasibility for engineering applications.
{"title":"Classification of External Vibration Sources through Data-Driven Models Using Hybrid CNNs and LSTMs","authors":"Ruihua Liang, Weifeng Liu, S. Kaewunruen, Hougui Zhang, Zongzhen Wu","doi":"10.1155/2023/1900447","DOIUrl":"https://doi.org/10.1155/2023/1900447","url":null,"abstract":"Excessive external vibrations could affect the normal functioning and integrity of sensitive buildings such as laboratories and heritage buildings. Usually, these buildings are exposed to multiple external vibration sources simultaneously, so the monitoring and respective evaluation of the vibration from various sources is necessary for the design of targeted vibration mitigation measures. To classify the sources of vibration accurately and efficiently, the advanced hybrid models of the convolutional neural network (CNN) and long short-term memory (LSTM) network were built in this study, and the models are driven by the extensive data of external vibration recorded in Beijing, and the parametric studies reveal that the proposed optimal model can achieve an accuracy of over 97% for the identification of external vibration sources. Finally, a real-world case study is presented, in which external vibration monitoring was carried out in a laboratory and the proposed CNN+LSTM model was used to identify the sources of vibration in the monitoring so that the impact of vibration from each source on the laboratory was analyzed statistically in detail. The results demonstrate the necessity of this study and its feasibility for engineering applications.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"250 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77661553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research examines the electromechanical characteristics of self-sensing cement composite (SCC) containing activated charcoal (AC) at various concentrations (5%, 10%, 15%, 20%, and 25%). The developed composite is placed in various zones in the beam to monitor beam behaviour, as well as in the column center to monitor column deflection. The research reveals that AC reduced the compressive strength and resistivity of cement composites by generating local hydration and carbon accumulation. The performance index approach optimizes AC concentrations at 25% and 20% without and with 10% silica fume (SF), respectively. The embedded SCC has monitored the deflection of beams and columns with a maximum correlation between electrical resistivity and deflection at 99% and 96%, respectively. According to the findings, the AC can generate SCC, which might be utilized to monitor the deflection of beams and columns.
{"title":"Concurrent Prospects to Develop Activated Charcoal Reinforced Self-Sensing Cement Composites for Structural Health Monitoring Applications","authors":"A. Dinesh, D. Suji, M. Pichumani","doi":"10.1155/2023/9731995","DOIUrl":"https://doi.org/10.1155/2023/9731995","url":null,"abstract":"This research examines the electromechanical characteristics of self-sensing cement composite (SCC) containing activated charcoal (AC) at various concentrations (5%, 10%, 15%, 20%, and 25%). The developed composite is placed in various zones in the beam to monitor beam behaviour, as well as in the column center to monitor column deflection. The research reveals that AC reduced the compressive strength and resistivity of cement composites by generating local hydration and carbon accumulation. The performance index approach optimizes AC concentrations at 25% and 20% without and with 10% silica fume (SF), respectively. The embedded SCC has monitored the deflection of beams and columns with a maximum correlation between electrical resistivity and deflection at 99% and 96%, respectively. According to the findings, the AC can generate SCC, which might be utilized to monitor the deflection of beams and columns.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73918160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A stacked ensemble learning model is developed to predict the modal parameters of space grid steel structures under environmental effects. Potential damage is detected via statistical analysis of the prediction residuals. For this purpose, five standalone heterogeneous machine learning models were trained for predicting natural frequencies; each model used the principal components of the environmental data as input parameters. Next, a stacked ensemble learner was built using the outputs of the five standalone models as its inputs. Finally, a damage indicator combining the predicted residuals of multiple orders of natural frequencies is proposed and statistically analyzed for accurate damage detection. To verify the effectiveness of the proposed method, a space grid model was created in the field environment and measured for a period. Dynamic and environmental data were collected, such as ambient temperature, humidity, wind speed and direction, and structural surface temperature. An automated procedure of the covariance-driven stochastic subspace identification method was conducted to identify bulk mode. The environmental dependence of the natural frequencies, damping ratios, and vibration modes was analyzed. Then, the method was validated based on short-term monitoring data from the baseline health state and unknown future states. The results show that the natural frequencies and damping ratios of space grid structures fluctuate significantly on a daily basis due to environmental influences. Stacked ensemble learning utilizes predictions from multiple heterogeneous models to produce a better predictive model. The statistical analysis of the prediction residuals by ensemble learning effectively removes the environmental influences, allowing for timely structural damage detection.
{"title":"Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning","authors":"Qinghua Han, Qian Ma, Dazhi Dang, Jie Xu","doi":"10.1155/2023/5687265","DOIUrl":"https://doi.org/10.1155/2023/5687265","url":null,"abstract":"A stacked ensemble learning model is developed to predict the modal parameters of space grid steel structures under environmental effects. Potential damage is detected via statistical analysis of the prediction residuals. For this purpose, five standalone heterogeneous machine learning models were trained for predicting natural frequencies; each model used the principal components of the environmental data as input parameters. Next, a stacked ensemble learner was built using the outputs of the five standalone models as its inputs. Finally, a damage indicator combining the predicted residuals of multiple orders of natural frequencies is proposed and statistically analyzed for accurate damage detection. To verify the effectiveness of the proposed method, a space grid model was created in the field environment and measured for a period. Dynamic and environmental data were collected, such as ambient temperature, humidity, wind speed and direction, and structural surface temperature. An automated procedure of the covariance-driven stochastic subspace identification method was conducted to identify bulk mode. The environmental dependence of the natural frequencies, damping ratios, and vibration modes was analyzed. Then, the method was validated based on short-term monitoring data from the baseline health state and unknown future states. The results show that the natural frequencies and damping ratios of space grid structures fluctuate significantly on a daily basis due to environmental influences. Stacked ensemble learning utilizes predictions from multiple heterogeneous models to produce a better predictive model. The statistical analysis of the prediction residuals by ensemble learning effectively removes the environmental influences, allowing for timely structural damage detection.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77806133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing rate of urbanization in recent decades has resulted in a global surge in the construction of slender high-rise buildings. These structures are prone to excessive wind-induced lateral vibrations in the crosswind direction owing to vortex shedding effects, causing occupant discomfort and, ultimately, dynamic serviceability failure. To reconcile the worldwide accelerated trend in constructing tall buildings with the sustainable building sector agenda, this paper proposes a novel bi-objective integrated design framework that leverages dynamic vibration absorbers (DVAs) to minimize the required material usage in the wind load-resisting structural systems (WLSSs) of occupant comfort-governed tall buildings. The framework couples structural sizing optimization for minimum-weight WLSS design (objective 1), with optimal DVA tuning for floor acceleration minimization to satisfy codified wind comfort design requirements by using the smallest DVA inertia (objective 2). Furthermore, a versatile numerical strategy is devised for the efficient solution of the proposed bi-objective optimization problem. For illustration, the framework is applied to a 15-storey steel building equipped with one of two different DVAs: a widely considered top-floor tuned mass damper (TMD) and an innovative ground-floor tuned inerter damper (TID). The derived Pareto optimal integrated (WLSS-plus-DVA) designs demonstrate that significant reduction in both structural steel usage and embodied carbon emissions can be achieved using either one of the two DVAs with moderate inertia. It is concluded that the proposed optimization-driven design framework and numerical solution strategy offer an alternative innovative approach to achieve material-efficient high-rise buildings under wind hazards.
{"title":"A Novel Integrated Optimization-Driven Design Framework for Minimum-Weight Lateral-Load Resisting Systems in Wind-Sensitive Buildings Equipped with Dynamic Vibration Absorbers","authors":"Zixiao Wang, A. Giaralis","doi":"10.1155/2023/3754773","DOIUrl":"https://doi.org/10.1155/2023/3754773","url":null,"abstract":"The increasing rate of urbanization in recent decades has resulted in a global surge in the construction of slender high-rise buildings. These structures are prone to excessive wind-induced lateral vibrations in the crosswind direction owing to vortex shedding effects, causing occupant discomfort and, ultimately, dynamic serviceability failure. To reconcile the worldwide accelerated trend in constructing tall buildings with the sustainable building sector agenda, this paper proposes a novel bi-objective integrated design framework that leverages dynamic vibration absorbers (DVAs) to minimize the required material usage in the wind load-resisting structural systems (WLSSs) of occupant comfort-governed tall buildings. The framework couples structural sizing optimization for minimum-weight WLSS design (objective 1), with optimal DVA tuning for floor acceleration minimization to satisfy codified wind comfort design requirements by using the smallest DVA inertia (objective 2). Furthermore, a versatile numerical strategy is devised for the efficient solution of the proposed bi-objective optimization problem. For illustration, the framework is applied to a 15-storey steel building equipped with one of two different DVAs: a widely considered top-floor tuned mass damper (TMD) and an innovative ground-floor tuned inerter damper (TID). The derived Pareto optimal integrated (WLSS-plus-DVA) designs demonstrate that significant reduction in both structural steel usage and embodied carbon emissions can be achieved using either one of the two DVAs with moderate inertia. It is concluded that the proposed optimization-driven design framework and numerical solution strategy offer an alternative innovative approach to achieve material-efficient high-rise buildings under wind hazards.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86575434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The hysteretic damping tuned mass damper (HD-TMD) is composed of a spring element, a hysteretic damping (HD) element, and a mass. The HD force is proportional to the displacement of the tuned mass damper (TMD). Recently, the application of HD-TMD has emerged, but its optimal design is still lacking. To fill this academic gap, numerical solutions for optimal parameters of HD-TMD subjected to white-noise excitation were obtained based on the H2 optimization criterion. Performance balance optimization with a weighting factor was carried out to improve the response of a structure with the HD-TMD system. A set of earthquake records and harmonic excitations were conducted to prove the effectiveness of the optimal numerical solutions and the performance balance design. It was found that the performance of the HD-TMD is slightly better than that of the traditional optimized TMD. As a real TMD application of HD-TMD, the variable friction pendulum TMD (VFP-TMD) was selected to experience earthquakes with the proposed optimal methods. Results showed that the optimal solutions provided the best performance but raised the problem of difficulty in maintaining linearity with a large displacement. Nevertheless, the performance balance design helped reduce this defect and provided impressive seismic mitigation capacity. Compared with the optimal numerical solution results, the performance balance design demonstrated 2.847% of loss in the maximum structural displacement reduction rate and 3.709% of loss in the root mean square reduction rate during the earthquake-excited period, respectively.
{"title":"Seismic Optimization for Hysteretic Damping-Tuned Mass Damper (HD-TMD) Subjected to White-Noise Excitation","authors":"Y. Xiang, P. Tan, Hui He, Qianmin Chen","doi":"10.1155/2023/1465042","DOIUrl":"https://doi.org/10.1155/2023/1465042","url":null,"abstract":"The hysteretic damping tuned mass damper (HD-TMD) is composed of a spring element, a hysteretic damping (HD) element, and a mass. The HD force is proportional to the displacement of the tuned mass damper (TMD). Recently, the application of HD-TMD has emerged, but its optimal design is still lacking. To fill this academic gap, numerical solutions for optimal parameters of HD-TMD subjected to white-noise excitation were obtained based on the H2 optimization criterion. Performance balance optimization with a weighting factor was carried out to improve the response of a structure with the HD-TMD system. A set of earthquake records and harmonic excitations were conducted to prove the effectiveness of the optimal numerical solutions and the performance balance design. It was found that the performance of the HD-TMD is slightly better than that of the traditional optimized TMD. As a real TMD application of HD-TMD, the variable friction pendulum TMD (VFP-TMD) was selected to experience earthquakes with the proposed optimal methods. Results showed that the optimal solutions provided the best performance but raised the problem of difficulty in maintaining linearity with a large displacement. Nevertheless, the performance balance design helped reduce this defect and provided impressive seismic mitigation capacity. Compared with the optimal numerical solution results, the performance balance design demonstrated 2.847% of loss in the maximum structural displacement reduction rate and 3.709% of loss in the root mean square reduction rate during the earthquake-excited period, respectively.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84407768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoxiong Zhang, Jia He, Xugang Hua, Zhengqing Chen, Z. Feng
Online identification of time-variant parameters without knowledge of external loads is an important but challenging task for structural health monitoring and vibration control. In this study, a two-stage approach, named extended Kalman filter with forgetting factor matrix under unknown inputs (EKF-FFM-UI), is proposed for simultaneously identifying the time-variant parameters and external loads. In stage 1, an extended Kalman filter under unknown inputs (EKF-UI) approach previously proposed by the authors is employed for estimating the structural states and unknown loads. This EKF-UI approach is solely suitable for time-invariant system identification. Therefore, the aim of stage 2 is to improve this approach for the purpose of possessing tracking capability. In this stage, the acceleration responses are first reconstructed by using the differential equation of motion and employed for improving the accuracy of estimated structural states. A forgetting factor matrix is introduced into the priori estimation error covariance matrix to track time-varying parameters. The square errors between the measurements and the corresponding estimates are defined as an index and used for detecting the damage time instant. Then, a covariance resetting technique is employed to assure that such changes in structural parameters can be efficiently captured. A shear-type building structure without/with magneto-rheological (MR) dampers and a fixed beam structure are used as numerical examples for validating the effectiveness of the proposed approach. Experimental tests on a six-story building model are also conducted. Results show the time-varying parameters and unknown inputs can be simultaneously identified with acceptable accuracy.
{"title":"Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation","authors":"Xiaoxiong Zhang, Jia He, Xugang Hua, Zhengqing Chen, Z. Feng","doi":"10.1155/2023/8379183","DOIUrl":"https://doi.org/10.1155/2023/8379183","url":null,"abstract":"Online identification of time-variant parameters without knowledge of external loads is an important but challenging task for structural health monitoring and vibration control. In this study, a two-stage approach, named extended Kalman filter with forgetting factor matrix under unknown inputs (EKF-FFM-UI), is proposed for simultaneously identifying the time-variant parameters and external loads. In stage 1, an extended Kalman filter under unknown inputs (EKF-UI) approach previously proposed by the authors is employed for estimating the structural states and unknown loads. This EKF-UI approach is solely suitable for time-invariant system identification. Therefore, the aim of stage 2 is to improve this approach for the purpose of possessing tracking capability. In this stage, the acceleration responses are first reconstructed by using the differential equation of motion and employed for improving the accuracy of estimated structural states. A forgetting factor matrix is introduced into the priori estimation error covariance matrix to track time-varying parameters. The square errors between the measurements and the corresponding estimates are defined as an index and used for detecting the damage time instant. Then, a covariance resetting technique is employed to assure that such changes in structural parameters can be efficiently captured. A shear-type building structure without/with magneto-rheological (MR) dampers and a fixed beam structure are used as numerical examples for validating the effectiveness of the proposed approach. Experimental tests on a six-story building model are also conducted. Results show the time-varying parameters and unknown inputs can be simultaneously identified with acceptable accuracy.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83351417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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