This research aims to develop a methodology for applying the geostatistical method to generate a groutability classification for granular soils.To ensure the precision of the suggested technique,a total of 103 data sa...This research aims to develop a methodology for applying the geostatistical method to generate a groutability classification for granular soils.To ensure the precision of the suggested technique,a total of 103 data samples were used.Predicting the groutability of granular soils has always been difficult because of many soil characteristics.As a result,a new two-dimensional graph,the groutability classification of granular soil(GCS)chart,was developed.GCS establishment was based on data analysis of the grain size of soil and cement-based grouts(N1 and N2),relative density(Dr)and fines content of the soil(FC),water/cement ratio of grout mixture(w/c),and grouting pressure(P),all of which have a direct impact on the groutability of soil media.The geostatistical method was used to develop and compile the GCS graph based on the aforementioned parameters with the use of coefficient S,which is a coefficient of the scoring set of parameters including P,w/c,Dr,and FC.The validation process was carried out hierarchically,with an additional set of 30 data.The proposed method has a prediction accuracy of roughly 96.7%,demonstrating a helpful tool.The proposed approach can be easily implemented in practical engineering situations because it has a comparable syntax to commonly used formulae.It should be noted that the proposed formula was only tested using the data samples collected,and the applicability of the produced procedure to other situations requires more examination.展开更多
Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas.Inadequate knowledge and lack of characterizing information prevent enginee...Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas.Inadequate knowledge and lack of characterizing information prevent engineers and experts from achieving appropriate prediction results related to the rockburst behaviour.In this study,a data set including 220 rockburst instances was collected for rockburst classification via the geostatistical method.An update of the 2D graph,the tunnel rockburst classification(TRC)chart,was introduced based on analysing three indicators,namely,elastic energy index(Wet),tangential stress in rock mass(σ_(0)),and uniaxial compressive strength(σ_(c)).Distribution and correlation of data were drawn on 2D plot,and the boundaries of rockburst were distinguished according to the achieved interpolate points by kriging method.Hierarchically,the validation phase was performed using an additional set of 28 case histories obtained from several projects around the world.The results showed that the TRC chart with an average error percentage of 3.6%in the prediction of rockburst had a significant and effective implementation in comparison to the exiting heuristic systems.Despite the initial character of the prediction,the described chart may be a helpful tool in the first steps of design and construction.展开更多
文摘This research aims to develop a methodology for applying the geostatistical method to generate a groutability classification for granular soils.To ensure the precision of the suggested technique,a total of 103 data samples were used.Predicting the groutability of granular soils has always been difficult because of many soil characteristics.As a result,a new two-dimensional graph,the groutability classification of granular soil(GCS)chart,was developed.GCS establishment was based on data analysis of the grain size of soil and cement-based grouts(N1 and N2),relative density(Dr)and fines content of the soil(FC),water/cement ratio of grout mixture(w/c),and grouting pressure(P),all of which have a direct impact on the groutability of soil media.The geostatistical method was used to develop and compile the GCS graph based on the aforementioned parameters with the use of coefficient S,which is a coefficient of the scoring set of parameters including P,w/c,Dr,and FC.The validation process was carried out hierarchically,with an additional set of 30 data.The proposed method has a prediction accuracy of roughly 96.7%,demonstrating a helpful tool.The proposed approach can be easily implemented in practical engineering situations because it has a comparable syntax to commonly used formulae.It should be noted that the proposed formula was only tested using the data samples collected,and the applicability of the produced procedure to other situations requires more examination.
文摘Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas.Inadequate knowledge and lack of characterizing information prevent engineers and experts from achieving appropriate prediction results related to the rockburst behaviour.In this study,a data set including 220 rockburst instances was collected for rockburst classification via the geostatistical method.An update of the 2D graph,the tunnel rockburst classification(TRC)chart,was introduced based on analysing three indicators,namely,elastic energy index(Wet),tangential stress in rock mass(σ_(0)),and uniaxial compressive strength(σ_(c)).Distribution and correlation of data were drawn on 2D plot,and the boundaries of rockburst were distinguished according to the achieved interpolate points by kriging method.Hierarchically,the validation phase was performed using an additional set of 28 case histories obtained from several projects around the world.The results showed that the TRC chart with an average error percentage of 3.6%in the prediction of rockburst had a significant and effective implementation in comparison to the exiting heuristic systems.Despite the initial character of the prediction,the described chart may be a helpful tool in the first steps of design and construction.
