Optical and visual measurement technology is used widely in fields that involve geometric measurements,and among such technology are laser and vision-based displacement measuring modules(LVDMMs).The displacement trans...Optical and visual measurement technology is used widely in fields that involve geometric measurements,and among such technology are laser and vision-based displacement measuring modules(LVDMMs).The displacement transformation coefficient(DTC)of an LVDMM changes with the coordinates in the camera image coordinate system during the displacement measuring process,and these changes affect the displacement measurement accuracy of LVDMMs in the full field of view(FFOV).To give LVDMMs higher accuracy in the FFOV and make them adaptable to widely varying measurement demands,a new calibration method is proposed to improve the displacement measurement accuracy of LVDMMs in the FFOV.First,an image coordinate system,a pixel measurement coordinate system,and a displacement measurement coordinate system are established on the laser receiving screen of the LVDMM.In addition,marker spots in the FFOV are selected,and the DTCs at the marker spots are obtained from calibration experiments.Also,a fitting method based on locally weighted scatterplot smoothing(LOWESS)is selected,and with this fitting method the distribution functions of the DTCs in the FFOV are obtained based on the DTCs at the marker spots.Finally,the calibrated distribution functions of the DTCs are applied to the LVDMM,and experiments conducted to verify the displacement measurement accuracies are reported.The results show that the FFOV measurement accuracies for horizontal and vertical displacements are better than±15μm and±19μm,respectively,and that for oblique displacement is better than±24μm.Compared with the traditional calibration method,the displacement measurement error in the FFOV is now 90%smaller.This research on an improved calibration method has certain significance for improving the measurement accuracy of LVDMMs in the FFOV,and it provides a new method and idea for other vision-based fields in which camera parameters must be calibrated.展开更多
The Iranian Guideline for Seismic Rehabilitation of Existing Buildings （GSREB）, which is currently used for vulnerability assessment of existing buildings in Iran, is evaluated in this paper. The vulnerability of sa...The Iranian Guideline for Seismic Rehabilitation of Existing Buildings （GSREB）, which is currently used for vulnerability assessment of existing buildings in Iran, is evaluated in this paper. The vulnerability of sample buildings of a variety stories with special steel moment resisting frames, designed according to the Standard No.2800 requirements, is assessed by GSREB. In the vulnerability assessment, different analysis methods were used and the results, in terms of usage ratio, defined as the ratio of the strength/deformation demand to the corresponding capacity, are compared. Numerical results show that some columns of these buildings do not satisfy the life safety performance criteria in the design hazard level. Moreover, the target displacement estimated by the Displacement Coefficient Method （DCM） is larger than the maximum displacement calculated by nonlinear dynamic analysis.展开更多
The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this...The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this study proposes three convolutional prediction models:CNN-LSTM-Attention,CNN-BiLSTM-Attention,and CNN-GRUAttention.The displacement coordination coefficient(DCC)index and stress curves were employed as input variables to evaluate the performance of each model in discriminating rock stability states under different data structures and input configurations.Furthermore,an innovative methodology for predicting rock failure time utilizing convolutional models was developed.The experimental results demonstrate that the CNN-LSTMAttention model,utilizing a 10×10×2 data structure,exhibits superior performance in rock stability state discrimination,achieving an accuracy of 95.25%on the validation set and a recall rate of 96%for samples in high-risk areas.Furthermore,when the DCC index was used as the input variable,the CNN-LSTM-Attention model achieved recall rates of 95.8%and 86.5%for medium-and high-risk areas,respectively,in the validation set.These findings indicate that the proposed convolutional models can effectively identify rock stability states by leveraging surface deformation characteristics.The CNN-LSTM-Attention model,with the DCC index as the input variable,is capable of predicting the final rock failure time in real-time once the DCC abrupt change exceeds 0.78.For different rocks,the model can predict the failure time within 20 s after the DCC reaches 0.78,with an error rate of less than 9%.The convolutional neural network model,developed based on the DCC index,provides a novel methodological approach for geohazard early warning research,facilitating slope instability monitoring and earthquake precursor identification using GNSS and other displacement measurement techniques.展开更多
基金supported financially by the National Natural Science Foundation of China (NSFC) (Grant No.51775378)the Key Projects in Tianjin Science&Technology Support Program (Grant No.19YFZC GX00890).
文摘Optical and visual measurement technology is used widely in fields that involve geometric measurements,and among such technology are laser and vision-based displacement measuring modules(LVDMMs).The displacement transformation coefficient(DTC)of an LVDMM changes with the coordinates in the camera image coordinate system during the displacement measuring process,and these changes affect the displacement measurement accuracy of LVDMMs in the full field of view(FFOV).To give LVDMMs higher accuracy in the FFOV and make them adaptable to widely varying measurement demands,a new calibration method is proposed to improve the displacement measurement accuracy of LVDMMs in the FFOV.First,an image coordinate system,a pixel measurement coordinate system,and a displacement measurement coordinate system are established on the laser receiving screen of the LVDMM.In addition,marker spots in the FFOV are selected,and the DTCs at the marker spots are obtained from calibration experiments.Also,a fitting method based on locally weighted scatterplot smoothing(LOWESS)is selected,and with this fitting method the distribution functions of the DTCs in the FFOV are obtained based on the DTCs at the marker spots.Finally,the calibrated distribution functions of the DTCs are applied to the LVDMM,and experiments conducted to verify the displacement measurement accuracies are reported.The results show that the FFOV measurement accuracies for horizontal and vertical displacements are better than±15μm and±19μm,respectively,and that for oblique displacement is better than±24μm.Compared with the traditional calibration method,the displacement measurement error in the FFOV is now 90%smaller.This research on an improved calibration method has certain significance for improving the measurement accuracy of LVDMMs in the FFOV,and it provides a new method and idea for other vision-based fields in which camera parameters must be calibrated.
文摘The Iranian Guideline for Seismic Rehabilitation of Existing Buildings （GSREB）, which is currently used for vulnerability assessment of existing buildings in Iran, is evaluated in this paper. The vulnerability of sample buildings of a variety stories with special steel moment resisting frames, designed according to the Standard No.2800 requirements, is assessed by GSREB. In the vulnerability assessment, different analysis methods were used and the results, in terms of usage ratio, defined as the ratio of the strength/deformation demand to the corresponding capacity, are compared. Numerical results show that some columns of these buildings do not satisfy the life safety performance criteria in the design hazard level. Moreover, the target displacement estimated by the Displacement Coefficient Method （DCM） is larger than the maximum displacement calculated by nonlinear dynamic analysis.
基金supported by the National Natural Science Foundation of China(No.52474106).
文摘The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this study proposes three convolutional prediction models:CNN-LSTM-Attention,CNN-BiLSTM-Attention,and CNN-GRUAttention.The displacement coordination coefficient(DCC)index and stress curves were employed as input variables to evaluate the performance of each model in discriminating rock stability states under different data structures and input configurations.Furthermore,an innovative methodology for predicting rock failure time utilizing convolutional models was developed.The experimental results demonstrate that the CNN-LSTMAttention model,utilizing a 10×10×2 data structure,exhibits superior performance in rock stability state discrimination,achieving an accuracy of 95.25%on the validation set and a recall rate of 96%for samples in high-risk areas.Furthermore,when the DCC index was used as the input variable,the CNN-LSTM-Attention model achieved recall rates of 95.8%and 86.5%for medium-and high-risk areas,respectively,in the validation set.These findings indicate that the proposed convolutional models can effectively identify rock stability states by leveraging surface deformation characteristics.The CNN-LSTM-Attention model,with the DCC index as the input variable,is capable of predicting the final rock failure time in real-time once the DCC abrupt change exceeds 0.78.For different rocks,the model can predict the failure time within 20 s after the DCC reaches 0.78,with an error rate of less than 9%.The convolutional neural network model,developed based on the DCC index,provides a novel methodological approach for geohazard early warning research,facilitating slope instability monitoring and earthquake precursor identification using GNSS and other displacement measurement techniques.
