Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Assessing environmental factors associated with regional schistosomiasis prevalence in Anhui Province,Peoples’Republic of China using a geographical detector method

Background:Schistosomiasis is a water-borne disease caused by trematode worms belonging to genus Schistosoma,which is prevalent most of the developing world.Transmission of the disease is usually associated with multiple biological characteristics and social factors but also factors can play a role.Few studies have assessed the exact and interactive influence of each factor promoting schistosomiasis transmission.Methods:We used a series of different detectors(i.e.,specific detector,risk detector,ecological detector and interaction detector)to evaluate separate and interactive effects of the environmental factors on schistosomiasis prevalence.Specifically,(i)specific detector quantifies the impact of a risk factor on an observed spatial disease pattern,which were ranked statistically by a value of Power of Determinate(PD)calculation;(ii)risk detector detects high risk areas of a disease on the condition that the study area is stratified by a potential risk factor;(iii)ecological detector explores whether a risk factor is more significant than another in controlling the spatial pattern of a disease;(iv)interaction detector probes whether two risk factors when taken together weaken or enhance one another,or whether they are independent in developing a disease.Infection data of schistosomiasis based on conventional surveys were obtained at the county level from the health authorities in Anhui Province,China and used in combination with information from Chinese weather stations and internationally available environmental data.Results:The specific detector identified various factors of potential importance as follows:Proximity to Yangtze River(0.322)>Land cover(0.285)>sunshine hours(0.256)>population density(0.109)>altitude(0.090)>the normalized different vegetation index(NDVI)(0.077)>land surface temperature at daytime(LST_(day))(0.007).The risk detector indicated that areas of schistosomiasis high risk were located within a buffer distance of 50 km from Yangtze River.The ecological detector disclosed that the factors investigated have significantly different effects.The interaction detector revealed that interaction between the factors enhanced their main effects in most cases.Conclusion:Proximity to Yangtze River had the strongest effect on schistosomiasis prevalence followed by land cover and sunshine hours,while the remaining factors had only weak influence.Interaction between factors played an even more important role in influencing schistosomiasis prevalence than each factor on its own.High risk regions influenced by strong interactions need to be targeted for disease control intervention.