This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the infl...This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.展开更多
Thermal comfort is an important factor which affects both work efficiency and life quality. On the basis of satisfying the normal life of the crew and reliable work of equipment, the thermal comfort is increasingly pu...Thermal comfort is an important factor which affects both work efficiency and life quality. On the basis of satisfying the normal life of the crew and reliable work of equipment, the thermal comfort is increasingly pursued through the design of the environmental control system of modern craft. Thus, a comprehensive survey of the thermal comfort in the cockpit is carried out. First of all, factors affecting the thermal comfort in aircraft cabin are summarized, including low relative humidity, mean radiant temperature, colored light, human metabolic rate and gender, among which the first three factors are environmental factors and the other two are human factors. Although noise is not a factor affecting thermal comfort, it is an important factor in the overall satisfaction of the aircraft cabin environment. Then the thermal comfort prediction models are introduced, including thermal comfort models suitable for steady state uniform environment and thermal comfort models suitable for transient non-uniform environment. Then the limitations of the typical thermal comfort models applied to aircraft are discussed. Since the concept of thermal adaptation has been gradually accepted in recent years, many field studies on thermal adaptation have been carried out. Therefore, the adaptive thermal comfort models are summarized and analyzed systematically in this paper. At present, mixing ventilation(MV) system is widely used in most commercial aircraft. However, the air quality under the MV system is very poor, and contaminants cannot be effectively eliminated. So a noticeable shift is the design of ventilation system for cabin drawing lessons from the surface buildings. Currently, the most interesting question is that whether the traditional mixing ventilation(MV) system in an aircraft can be replaced by or combined with displacement ventilation(DV) system without decreasing thermal comfort. A reduction of energy consumption is a valuable gain. Additionally, various seat personalized ventilation systems have also been propose展开更多
Retrofitting a historic building under different national goals involves multiple objectives,constraints,and numerous potential measures and packages,therefore it is time-consuming and challenging during the early des...Retrofitting a historic building under different national goals involves multiple objectives,constraints,and numerous potential measures and packages,therefore it is time-consuming and challenging during the early design stage.This study introduces a systematic retrofitting approach that incorporates standard measures for the building envelope(walls,windows,roof),as well as the heating,cooling,and lighting systems.Three retrofit objectives are delineated based on prevailing Chinese standards.The retrofit measures function as genes to optimize energy-savings,carbon emissions,and net present value(NPV)by employing a log-additive decomposition approach through energy simulation techniques and NSGA-II,yielding 185,163,and 8 solutions.Subsequently,a weighted sum method is proposed to derive optimal solutions across multiple scenarios.The framework is applied to a courtyard building in Nanjing,China,and the outcomes of the implementation are scrutinized to ascertain the optimal retrofit package under various scenarios.Through this retrofit,energy consumption can be diminished by up to 63.62%,resulting in an NPV growth of 151.84%,and maximum rate of 60.48%carbon reduction.These three result values not only indicate that the optimal values are achieved in these three aspects of energy saving,carbon reduction and economy,but also show the possibility of possible equilibrium in this multi-objective optimization problem.The framework proposed in this study effectively addresses the multi-objective optimization challenge in building renovation by employing a reliable optimization algorithm with a computationally efficient reduced-order model.It provides valuable insights and recommendations for optimizing energy retrofit strategies and meeting various performance objectives.展开更多
基金supported by The Hong Kong Polytechnic University through the project RU3Ythe Research Grant Council through the project PolyU 5128/13E+1 种基金National Natural Science Foundation of China(Grant No.51778313)Cooperative Innovation Center of Engineering Construction and Safety in Shangdong Blue Economic Zone
文摘This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.
基金Supported by the National Natural Science Foundation of China (51106074)
文摘Thermal comfort is an important factor which affects both work efficiency and life quality. On the basis of satisfying the normal life of the crew and reliable work of equipment, the thermal comfort is increasingly pursued through the design of the environmental control system of modern craft. Thus, a comprehensive survey of the thermal comfort in the cockpit is carried out. First of all, factors affecting the thermal comfort in aircraft cabin are summarized, including low relative humidity, mean radiant temperature, colored light, human metabolic rate and gender, among which the first three factors are environmental factors and the other two are human factors. Although noise is not a factor affecting thermal comfort, it is an important factor in the overall satisfaction of the aircraft cabin environment. Then the thermal comfort prediction models are introduced, including thermal comfort models suitable for steady state uniform environment and thermal comfort models suitable for transient non-uniform environment. Then the limitations of the typical thermal comfort models applied to aircraft are discussed. Since the concept of thermal adaptation has been gradually accepted in recent years, many field studies on thermal adaptation have been carried out. Therefore, the adaptive thermal comfort models are summarized and analyzed systematically in this paper. At present, mixing ventilation(MV) system is widely used in most commercial aircraft. However, the air quality under the MV system is very poor, and contaminants cannot be effectively eliminated. So a noticeable shift is the design of ventilation system for cabin drawing lessons from the surface buildings. Currently, the most interesting question is that whether the traditional mixing ventilation(MV) system in an aircraft can be replaced by or combined with displacement ventilation(DV) system without decreasing thermal comfort. A reduction of energy consumption is a valuable gain. Additionally, various seat personalized ventilation systems have also been propose
基金the National Key R&D Program-Strategic Scientific and Technological Innovation Cooperation(#2022YFE0208600)National Science and Foundation of China(#52208011)+1 种基金the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone(HZQB-KCZYB-2020083)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_0034).
文摘Retrofitting a historic building under different national goals involves multiple objectives,constraints,and numerous potential measures and packages,therefore it is time-consuming and challenging during the early design stage.This study introduces a systematic retrofitting approach that incorporates standard measures for the building envelope(walls,windows,roof),as well as the heating,cooling,and lighting systems.Three retrofit objectives are delineated based on prevailing Chinese standards.The retrofit measures function as genes to optimize energy-savings,carbon emissions,and net present value(NPV)by employing a log-additive decomposition approach through energy simulation techniques and NSGA-II,yielding 185,163,and 8 solutions.Subsequently,a weighted sum method is proposed to derive optimal solutions across multiple scenarios.The framework is applied to a courtyard building in Nanjing,China,and the outcomes of the implementation are scrutinized to ascertain the optimal retrofit package under various scenarios.Through this retrofit,energy consumption can be diminished by up to 63.62%,resulting in an NPV growth of 151.84%,and maximum rate of 60.48%carbon reduction.These three result values not only indicate that the optimal values are achieved in these three aspects of energy saving,carbon reduction and economy,but also show the possibility of possible equilibrium in this multi-objective optimization problem.The framework proposed in this study effectively addresses the multi-objective optimization challenge in building renovation by employing a reliable optimization algorithm with a computationally efficient reduced-order model.It provides valuable insights and recommendations for optimizing energy retrofit strategies and meeting various performance objectives.
