Machine learning-based prediction of soil compression modulus with application of ID settlement

The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.

Guajamers A-I, Rearranged Polycyclic Phloroglucinol Meroterpenoids from Psidium guajava Leaves and Their Antibacterial Activity

Eight new polycyclic phloroglucinol meroterpenoids guajamers A-H(1-8),a methylated benzoylphloroglucinol meroterpenoid guajamer I(9)representing a new skeleton,and two known analogues(10 and 11)were isolated from the leaves of Psidium guajava.The structures of new molecules were elucidated by detailed analysis of spectroscopic data,and those of 1,2,8,and 9 were unambiguously confirmed by single-crystal X-ray diffraction study.Structurally,compounds 1-8 were sesquiterpene and monoterpene-based meroterpenoids with rearranged skeletons,while compound 9 was the first case of 3-alkyl-5-formyl-benzoylphloroglucinol-coupled sesquiterpene containing an unusual C-l-spiro-fused 6/6/9/4 polycyclic skeleton.In addition,all the isolated compounds were evaluated for their antibacterial activity against three bacterial strains,and most of them(compounds 2-7,10,and 11)showed antibacterial activity against Staphylococcus aureus and Staphylococcus epidermidis with MIC values of 8-32 μmol/L.These findings suggested that meroterpenoids isolated from Psidium guajava can be considered as potential antibacterial leading compounds for pharmaceutical industry.