Climate change is one environmental threat that poses great challenges to the future development prospects of Ethiopia. The study used the statistically downscaled daily data in 30-years intervals from the second gene...Climate change is one environmental threat that poses great challenges to the future development prospects of Ethiopia. The study used the statistically downscaled daily data in 30-years intervals from the second generation of the Earth System Model (CanESM2) under two Representative Concentration Pathways (RCPs): RCP 4.5 and RCP 8.5 for three future time slices;near-term (2010-2039), mid-century (2040-2069) and end-century (2071-2099) were generated. The observed data of maximum and minimum temperature and precipitation are a good simulation with the modeled data during the calibration and validation periods using the correlation coefficient (R<sup>2</sup>), the Nash-Sutcliffe efficiency (NSE), and the Root Mean Square Error (RMSE). The projected annual minimum and maximum temperatures are expected to increase by 0.091°C, 0.517°C, and 0.73°C and 0.072°C, 0.245°C, and 0.358°C in the 2020s, 2050s, and 2080s under the intermediate scenario, respectively. Under RCP8.5, the annual minimum and maximum temperatures are expected to increase by 0.192°C, 0.409°C, and 0.708°C, 0.402°C, 4.352°C, and 8.750°C in the 2020s, 2050s, and 2080s, respectively. Besides, the precipitation is expected to increase under intermediate and high emission scenarios by 1.314%, 7.643%, and 12.239%, and 1.269%, 10.316% and 26.298% in the 2020s, 2050s, and 2080s, respectively. Temperature and precipitation are projected to increase in total amounts under all-time slices and emissions pathways. In both emission scenarios, the greatest changes in maximum temperature, minimum temperature, and precipitation are predicted by the end of the century. This implies climate smart actions in development policies and activities need to consider locally downscale expected climatic changes.展开更多
Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resou...Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resources.Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency.Deep learning models(DLMs)stand out as an effective solution in crack detection due to their features such as end-to-end learning capability,model adaptation,and automatic learning processes.However,providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic.In this article,three different methods are proposed for detecting cracks in concrete structures.In the first method,a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network(SCAMEFNet)deep neural network,which has a deep architecture and can provide a balance between the depth of DLMs and model parameters,has been developed.This model was designed using a convolutional neural network,multi-head attention,and various fusion techniques.The second method proposes a modified vision transformer(ViT)model.A two-stage ensemble learning model,deep featurebased two-stage ensemble model(DFTSEM),is proposed in the third method.In this method,deep features and machine learning methods are used.The proposed approaches are evaluated using the Concrete Cracks Image Data set,which the authors collected and contains concrete cracks on building surfaces.The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%,the ViT model 97.33%,and the DFTSEM model 99.00%.These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to realworld problems.In addition,the developed methods can contribute as a tool for BIM platforms in smart cities for building health.展开更多
文摘Climate change is one environmental threat that poses great challenges to the future development prospects of Ethiopia. The study used the statistically downscaled daily data in 30-years intervals from the second generation of the Earth System Model (CanESM2) under two Representative Concentration Pathways (RCPs): RCP 4.5 and RCP 8.5 for three future time slices;near-term (2010-2039), mid-century (2040-2069) and end-century (2071-2099) were generated. The observed data of maximum and minimum temperature and precipitation are a good simulation with the modeled data during the calibration and validation periods using the correlation coefficient (R<sup>2</sup>), the Nash-Sutcliffe efficiency (NSE), and the Root Mean Square Error (RMSE). The projected annual minimum and maximum temperatures are expected to increase by 0.091°C, 0.517°C, and 0.73°C and 0.072°C, 0.245°C, and 0.358°C in the 2020s, 2050s, and 2080s under the intermediate scenario, respectively. Under RCP8.5, the annual minimum and maximum temperatures are expected to increase by 0.192°C, 0.409°C, and 0.708°C, 0.402°C, 4.352°C, and 8.750°C in the 2020s, 2050s, and 2080s, respectively. Besides, the precipitation is expected to increase under intermediate and high emission scenarios by 1.314%, 7.643%, and 12.239%, and 1.269%, 10.316% and 26.298% in the 2020s, 2050s, and 2080s, respectively. Temperature and precipitation are projected to increase in total amounts under all-time slices and emissions pathways. In both emission scenarios, the greatest changes in maximum temperature, minimum temperature, and precipitation are predicted by the end of the century. This implies climate smart actions in development policies and activities need to consider locally downscale expected climatic changes.
文摘Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resources.Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency.Deep learning models(DLMs)stand out as an effective solution in crack detection due to their features such as end-to-end learning capability,model adaptation,and automatic learning processes.However,providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic.In this article,three different methods are proposed for detecting cracks in concrete structures.In the first method,a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network(SCAMEFNet)deep neural network,which has a deep architecture and can provide a balance between the depth of DLMs and model parameters,has been developed.This model was designed using a convolutional neural network,multi-head attention,and various fusion techniques.The second method proposes a modified vision transformer(ViT)model.A two-stage ensemble learning model,deep featurebased two-stage ensemble model(DFTSEM),is proposed in the third method.In this method,deep features and machine learning methods are used.The proposed approaches are evaluated using the Concrete Cracks Image Data set,which the authors collected and contains concrete cracks on building surfaces.The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%,the ViT model 97.33%,and the DFTSEM model 99.00%.These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to realworld problems.In addition,the developed methods can contribute as a tool for BIM platforms in smart cities for building health.
