Zhilin Bai’s research while affiliated with Harbin Institute of Technology and other places

... The use of CNNs is, very likely, the most widely adopted strategy for assessing building damage in post-earthquake scenarios, with numerous recent studies published on the topic [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Various network architectures based on AlexNet [31], previously recognized as a significant milestone in computer vision, were developed, alongside many other CNN architectures [30]. ...

Reference:

Effectiveness of Generative AI for Post-Earthquake Damage Assessment

... Under suitable temperature, humidity and CO2 concentration conditions, the hydration products inside aerated concrete undergo a chemical reaction with the CO2 that penetrates into the material, generating stable calcium carbonate (CaCO₃). This carbonization process not only leads to changes in the microstructure and phase composition of the material, but also has a profound impact on physical properties such as compressive strength, dry density, shrinkage and thermal conductivity [8][9][10][11] In addition, the hydration products formed inside aerated concrete have a high carbonization activity, accelerating the curing of CO2 and further highlighting its potential in carbon sequestration [12]. ...

Reference:

A novel model for measuring the CO2 absorption flux of aerated concrete

... They pre-process the data and train a deep-learning model using CNNs to classify damaged and undamaged crops (Vimal et al. 2023). To assess the model's effectiveness and accuracy in identifying crop damage, field surveys will yield ground truth data (Bai et al. 2023) in agricultural and disaster management fields. The field of agriculture and disaster management. ...

Reference:

Deep learning for rapid crop damage assessment after cyclones

... The model can provide certain auxiliary decision support for the assessment of target destruction effect and battlefield damage assessment for both offense and defense in exercise drills, and has certain inspiration and reference significance for the development of artificial intelligence for target destruction effect assessment. Zou et al. [45] established a damage degree assessment method based on target detection and identification of component failure modes. A quantitative structural damage level assessment method was developed based on the damage type and degree of the component. ...

Reference:

A review of artificial intelligence techniques in blast field shockwave pressure testing