Failure criteria of unreinforced masonry walls of rural buildings under the impact of flash floods in mountainous regions

Damage to rural buildings in mountainous regions caused by flash floods accounts for a significant proportion of economic losses from disasters.The unreinforced masonry(URM)wall is the most vulnerable structural element of rural buildings exposed to flash floods.The failure of a URM wall indicates damage to rural buildings in flash floods.Based on the yield line theory of out-of-plane damage of URM walls and the virtual work method,brittle failure criteria for URM walls under the impact of flash floods were established.According to the field investigation data of the 26 June 2020 flash flood event in Damawu Gully and the corresponding simulation results of FLO-2D,the disaster-causing process was analysed,and the failure criteria were validated.Three building parameters were identified to influence the flood-resistance of URM walls,including the mortar grade,the span-to-height ratio of the wall,and the number of floors of the rural building.The results showed that the cause of the 26June disaster was the diversion of a 50-year flash flood into the residential community on the alluvial fan.The affected buildings were constructed with hollow blocks and lacked flood-resistance reinforcement.The critical failure depth of a URM wall restrained at the top by ring beams(RBs)under hydrostatic load conditions is 1.17 to 1.20 times greater than that of a URM wall without RBs,and the difference is even more pronounced when lowerstrength mortar is used.The flood-resistance of a URM wall constructed with Mb 7.5 mortar and restrained by RBs is almost as strong as that of a URM wall constructed with Mb 20 mortar and without RBs.The span-to-height ratio of a URM wall should not be greater than 1.875 in this case.However,the flood-resistance of a URM wall with RB restraint is almost independent of the span-to-height ratio.The brittle fracture energy of masonry mortar is more crucial to the flood-resistance of 4-edge restrained URM walls if L/Z>1.875.The flood-resistance of the URM wall of the first storey increases linearly with the number of floors.Single-storey rural buildings should be given priority to the use of high-grade masonry mortar and high-density blocks to improve flood-resistance.The failure criteria and the influence laws of building parameters on the flood-resistance of URM walls can provide references for flash flood mitigation and flood-resistance reinforcement of rural buildings in mountainous regions of Southwest China.

Investigating the Problems and Proposing Solutions for Rural Residential Buildings in Northwest China

With the increasing income of the rural population in Northwest China,people’s sense of happiness and gain is also increasing.However,the improvement of housing conditions is lagging behind the increasing income levels.In recent years,the government and owners have paid much attention to this issue.The local government has successively issued relevant guidelines and policies to encourage owners to reconstruct their houses and improve their housing conditions.Before improving housing conditions,the existing housing problems must be identified.In order to have a comprehensive and in-depth understanding of the problems of rural residential buildings in Northwest China and to propose corresponding solutions,90 representative rural residential buildings of three provinces in Northwest China have been selected for investigation,and mass data have been obtained.Upon analyzing these data,it has been found that there are problems in five aspects of rural residential buildings in Northwest China:architectural style and functional space layout,energy utilization and dissipation,design,construction,as well as structural safety and seismic measures.Valid solutions are proposed to address these problems,which can also provide a reference for the government and enterprises engaged in the architecture,engineering,and construction(AEC)industry in rural areas.

Research on Energy Consumption of Traditional Natural Villages in Transition: A Case Study in Zhejiang Province

Traditional agriculture is in the direction of increasing integration of the primary industry, secondary industry, and tertiary industry in Zhejiang province. A survey was undertaken on energy consumption of traditional natural villages by taking Anji Ligeng village for an example. This paper firstly studied rural buildings, rural family structure, occupants’ activity and the usage of household appliances in the form of a questionnaire. Then, the household energy resource structure and energy consumption structure were analyzed and compared with other surveys. The results show that, the electric energy consumption was 6 kWh/(m2•a), which was far less than urban residential household. In rural household energy resource structure, the proportion of non-commercial energy resource was higher than commercial energy resource. Firewood accounted for 83%, electricity for 12%, LPG for 3% and solar energy for 2%. In building energy consumption structure, cooking and hot water took up 33%, appliances 31%, lighting 20%, heating 12%, cooling 4%. In all influential factors, frequently used area, number of air conditioner per household and building function were obviously correlated with energy consumption;income, building shape factor and window to wall area ratio had no correlation with energy consumption in the low energy consumption area.

Automatic classification of rural building characteristics using deep learning methods on oblique photography

Rural building is important to the well-being of rural residents,leading to a significant need to carry out extensive surveys and retrofits of many rural buildings.On-site surveys by expert surveyors are currently the main approach,but this traditional method is often expensive and laborious,especially for large-scale survey tasks.Therefore,this study explores an alternative workflow based on deep learning(DL)methods to apply automatic classification of rural building characteristics.Taking four villages in Jizhou District of Tianjin,China as research samples,we tested selected convolutional neural network(CNN)architectures through the establishment of the training database containing 3258 labeled images,under the performance metrics of accuracy,recall and F1 score.The results showed that ResNet50 is the CNN architecture with the best performance,with the comprehensive consideration of overall metrics.Taking accuracy as the performance metric to test the generalization ability of ResNet50,the prediction results for seven building characteristic indicators from low to high are as follows:building function(0.827);building style(0.863);building quality(0.871);building age(0.880);building structure(0.891);abandoned or not(0.959);the number of storeys(0.995).Due to simplicity,accuracy and effectiveness,this workflow is transferable and cost-effective to investigate large-scale villages.

Energy consumption pattern and indoor thermal environment of residential building in rural China

With the continuous deepening of China’s rural construction and development,people’s living conditions are improved day by day,while accompanied by energy and environment crisis issues.This paper mainly analyzes the energy consumption pattern and the indoor environment of rural households in China and discusses the energy-saving optimization strategies for improving the thermal environment of buildings.Questionnaire surveys and field surveys were conducted in three villages in Guanghan,China.The measurement results show that the annual indoor temperature range of the region in the summer is 15-31℃and the relative humidity range is 34%-96%.The average indoor temperatures in summer and winter are 28℃and 16℃respectively.The indoor thermal environment of rural buildings is usually poor and cannot meet the requirements of Chinese standards.At the same time,the architectural design and energy consumption pattern of rural households are different from those in urban areas as countryside has unique characteristics.Finally,we put forward certain energy-saving improvement measures at the end of the article.