A systematic review of machine learning modeling processes and applications in ROP prediction in the past decade

Fossil fuels are undoubtedly important, and drilling technology plays an important role in realizing fossil fuel exploration;therefore, the prediction and evaluation of drilling efficiency is a key research goal in the industry. Limited by the unknown geological environment and complex operating procedures, the prediction and evaluation of drilling efficiency were very difficult before the introduction of machine learning algorithms. This review statistically analyses rate of penetration(ROP) prediction models established based on machine learning algorithms;establishes an overall framework including data collection, data preprocessing, model establishment, and accuracy evaluation;and compares the effectiveness of different algorithms in each link of the process. This review also compares the prediction accuracy of different machine learning models and traditional models commonly used in this field and demonstrates that machine learning models are the most effective technical means in current ROP prediction modeling.

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.

A predictive score for retinopathy of prematurity by using clinical risk factors and serum insulin-like growth factor-1 levels

AIM：To detect the impact of insulin-like growth factor-1（IGF-1）and other risk factors for the early prediction of retinopathy of prematurity（ROP）and to establish a scoring system for ROP prediction by using clinical criteria and serum IGF-1 levels.METHODS：The study was conducted with 127 preterm infants.IGF-1 levels in the 1st day of life,1st,2nd,3rd and4th week of life was analyzed.The score was established after logistic regression analysis,considering the impact of each variable on the occurrences of any stage ROP.A validation cohort containing 107 preterm infants was included in the study and the predictive ability of ROP score was calculated.RESULTS：Birth weights（BW）,gestational weeks（GW）and the prevalence of breast milk consumption were lower,respiratory distress syndrome（RDS）,bronchopulmonarydysplasia（BPD）and necrotizing enterocolitis（NEC）were more frequent,the duration of mechanical ventilation and oxygen supplementation was longer in patients with ROP（P〈0.05）.Initial serum IGF-1 levels tended to be lower in newborns who developed ROP.Logistic regression analysis revealed that low BW（〈1250 g）,presence of intraventricular hemorrhage（IVH）and formula feeding increased the risk of ROP.Afterwards,the scoring system was validated on 107 infants.The negative predictive values of a score less than 4 were 84.3%,74.7%and 79.8%while positive predictive values were 76.3%,65.5%and71.6%respectively.CONCLUSION：In addition to BW〈1250 g and IVH,formula consumption was detected as a risk factor for the development of ROP.Breastfeeding is important for prevention of ROP in preterm infants.