Engineering problems often involve large spatial scales and long-term lifespans.This makes it exceptionally difficult to measure engineering parameters and predict disasters such as slope instability or tunnel collaps...Engineering problems often involve large spatial scales and long-term lifespans.This makes it exceptionally difficult to measure engineering parameters and predict disasters such as slope instability or tunnel collapses.A key challenge is to calculate large-scale(target lifespan)quantitative indicators from small-scale(short-term)detectable results,thereby enhancing engineering safety and economic efficiency.Many engineering problems exhibit a unidirectional spatio-temporal evolution with either decay or enhancement as their spatial scale or time increases.This phenomenon is called the power law with exponential function.A general approach is required to use this evolution law in the prediction of the unknown from the known.This paper proposes a novel concept to calculate large-scale indicators via variation of small-scale data(called CLIVS for short),to address a general approach through the following five aspects:Firstly,general spatio-temporal evolution laws in engineering are systematically summarized and classified.Then,the core idea and basic concepts of CLIVS,its mathematical formulation and solution procedure are described in detail.Thirdly,the linkage of CLIVS to past famous philosophy schools is explored.Fourthly,the potential applications of CLIVS to rock mechanics and rock engineering are classified according to size effect and time-scale law.Finally,two typical examples of the application of CLIVS to engineering parameter prediction are presented.These demonstrate that the CLIVS provides a novel way and a general approach to accurately predict unknown behaviors based on known local spatial data or short-term indicators.It formulates a unified theoretical framework or universal approach to calculate unmeasurable engineering parameters or predict lifespan with reasonable accuracy from the knowns measurable at the local scale or in the short term.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.42530704 and 41427802)by the Key Project of Science and Technology Strategic Consulting of the Chinese Academy of Engineering(Grant No.2025 XZ 57).
文摘Engineering problems often involve large spatial scales and long-term lifespans.This makes it exceptionally difficult to measure engineering parameters and predict disasters such as slope instability or tunnel collapses.A key challenge is to calculate large-scale(target lifespan)quantitative indicators from small-scale(short-term)detectable results,thereby enhancing engineering safety and economic efficiency.Many engineering problems exhibit a unidirectional spatio-temporal evolution with either decay or enhancement as their spatial scale or time increases.This phenomenon is called the power law with exponential function.A general approach is required to use this evolution law in the prediction of the unknown from the known.This paper proposes a novel concept to calculate large-scale indicators via variation of small-scale data(called CLIVS for short),to address a general approach through the following five aspects:Firstly,general spatio-temporal evolution laws in engineering are systematically summarized and classified.Then,the core idea and basic concepts of CLIVS,its mathematical formulation and solution procedure are described in detail.Thirdly,the linkage of CLIVS to past famous philosophy schools is explored.Fourthly,the potential applications of CLIVS to rock mechanics and rock engineering are classified according to size effect and time-scale law.Finally,two typical examples of the application of CLIVS to engineering parameter prediction are presented.These demonstrate that the CLIVS provides a novel way and a general approach to accurately predict unknown behaviors based on known local spatial data or short-term indicators.It formulates a unified theoretical framework or universal approach to calculate unmeasurable engineering parameters or predict lifespan with reasonable accuracy from the knowns measurable at the local scale or in the short term.
