Li Li’s research while affiliated with Shanghai University and other places

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Publications (2)


Deterioration Model for Reinforced Concrete Bridge Girders Based on Survival Analysis
  • Article
  • Full-text available

November 2022

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81 Reads

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2 Citations

Mathematics

Li Li

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Yu Lu

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Miaojuan Peng

The prediction of bridge service performance is essential for bridge maintenance, operation, and decision making. As a key component of the superstructure, the performance of the main girders is critical to the structural safety of the bridge. This study makes full use of the inspection records from the Bridge Management System (BMS) in Shanghai and performs pre-processing work on a large amount of data. Recent advances in survival analysis were utilized to investigate the inspection records of over 40,000 reinforced concrete bridge main girders over a 14-year period. Survival analysis methods based on the Weibull distribution were used to predict the service performance of the main girders, and, in addition, a COX proportional hazards model was used to analyze the effect of different covariates on the survival of the main girders. The results show that the deterioration rate of main girders increases with age, with an average life of 87 years for main girders in Shanghai. The grade of the road on which the bridge is located and the position of the main girder in the bridge superstructure have a significant impact on the probability of survival of the main girder. It can be concluded that more attention should be paid to the inspection and maintenance of side girders on branch roads to reduce the pressure on bridge management in the future. Furthermore, the analysis in this study found that the deterioration rate of the main girders is faster than the deterioration rate of the whole bridge and superstructure, and, therefore, more attention and necessary preventive maintenance measures should be taken in the maintenance and management of the main girders.

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Figure 2. The smart helmet measurement process.
Figure 8. The MSE change curve of the model.
Results of covariance test for the influencing factors of infrared temperature measurement of smart helmets.
Statistical table of fitting effect of each model.
Test data used to validate the model.
A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

November 2021

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434 Reads

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12 Citations

Mathematics

In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.

Citations (2)


... Bridge deterioration modeling marks an integral pillar of maintenance management systems, and its accuracy inevitably signifies the efficaciousness of bridge management [15,16]. Hitherto, artificial intelligence (AI)-driven models established themselves as a powerful mechanism to anticipate future performance conditions of bridge elements. ...

Reference:

A Comprehensive Review of the Key Deterioration Factors of Concrete Bridge Decks
Deterioration Model for Reinforced Concrete Bridge Girders Based on Survival Analysis

Mathematics

... Por fim, não foram encontrados na literatura, dispositivos semelhantes que apresentem a integração em tempo real dos dados obtidos de temperatura e umidade para executar avaliações sobre o estresse térmico vivenciado pelo usuário com detecção de queda. Por exemplo, trabalhos como [18] e [19] apresentam capacetes inteligentes com instrumentação semelhante, porém a umidade em ambos os casos é utilizada para propósitos diversos e não para avaliação do estresse térmico. Em contrapartida, os dispositivos encontrados que apresentaram a detecção de queda, por vezes se limitaram somente a detecção de temperatura do usuário, como [16] ou ainda utilizaram medidas diferentes como sensores de batimentos cardíacos [17]. ...

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

Mathematics