An efficient physics-guided Bayesian framework for predicting ground settlement profile during excavations in clay

Recently,the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly.However,updating the ground settlement profile,which is crucial for determining potential damage to nearby infrastructures,has received limited attention.To address this,this paper proposes a physics-guided simplified model combined with a Bayesian updating framework to accurately predict the ground settlement profile.The advantage of this model is that it eliminates the need for complex finite element modeling and makes the updating framework user-friendly.Furthermore,the model is physically interpretable,which can provide valuable references for construction adjustments.The effectiveness of the proposed method is demonstrated through two field case studies,showing that it can yield satisfactory predictions for the settlement profile.

A New Probability of Detection Model for Updating Crack Distribution of Offshore Structures

There exists model uncertainty of probability of detection for inspecting ship structures with nondestructive inspection techniques. Based on a comparison of several existing probability of detection (POD) models, a new probability of detection model is proposed for the updating of crack size distribution. Furthermore, the theoretical derivation shows that most existing probability of detection models are special cases of the new probability of detection model. The least square method is adopted for determining the values of parameters in the new POD model. This new model is also compared with other existing probability of detection models. The results indicate that the new probability of detection model can fit the inspection data better. This new probability of detection model is then applied to the analysis of the problem of crack size updating for offshore structures. The Bayesian updating method is used to analyze the effect of probability of detection models on the posterior distribution of a crack size. The results show that different probabilities of detection models generate different posterior distributions of a crack size for offshore structures.

Integrate intere-well connectivity data with static reservoir models based on Bayesian formalism

The inter-well connectivity calculated from reservoir dynamic production data reflects formation heterogeneity quantitatively.Currently,the calculated inter-well connectivity between pair wells is mainly used as a tool for water flood management but not for quantitative reservoir characterization.This study proposes an innovative,dynamic data integration workflow that can integrate inter-well connectivity with a static reservoir model.In the workflow,the first step is calculating the inter-well connectivity vectors from the reservoir pairwise injector and producer wells.The second step covers interpolation in the domain of interest.The third step is to update the permeability model based on the Bayesian updating method.The result of this study shows that integrating the calculated inter-well connectivity with the static models enhances model reliability and it also provides an insight to deeper geological understanding reflected from dynamic data integration in reservoir modeling.

Repair tolerance assessment for aircraft composite structures using Bayesian updating

Detection and repair of composite damage is crucial to ensure the safety and reliability of aircraft structures.A novel approach to quantitatively evaluate the repair tolerance of composite structures in civil aircraft based on Bayesian updating is presented.The method incorporates historical damage inspection data to determine the prior distribution of damage size,which is then updated with newly collected damage size data using Bayesian theory.Monte Carlo simulation is employed to investigate the probability of failure and estimate maintenance costs,considering various factors such as the frequency and timing of damage events,damage detection,structural strength,gust loads,and maintenance expenses throughout the lifecycle of composite structures.Safety and economic factors are considered to establish a lower threshold for repairs and an upper threshold for maintenance based on the occurrence of accidental impact damage.Verification of the effectiveness and feasibility of a quantitative assessment method for repair tolerance is conducted using damage statistics data from civil aircraft routes utilizing the structural skin panels of composite outer wing.The results demonstrate that the method proposed in conjunction with extensive simulations and full utilization of field damage inspection data can effectively simulate unexpected impact damage situations that may occur during civil aircraft service and evaluate the reliability and economic feasibility of the repair of structure.The research findings hold significant theoretical and practical value for the preparation of documents for continued airworthiness of composite structures,including structural repair manuals and maintenance programs.

TWO-STAGE ORDERING DECISION FOR A SHORT-LIFE-CYCLE PRODUCT

This paper investigates the ordering decision problem for a short-life-cycle product under Bayesian updating. For a product characterized by a single manufacturing cycle and two selling periods, we depict a Two-Stage （TS） ordering strategy with a stochastic dynamic programming model in the view of the whole system, and prove that the expected profit function of the whole system is concave on the first ordering quantity and the remedial ordering quantity, respectively. Then, the optimal ordering decision is developed. Finally, characteristics of the optimal ordering quantities are analyzed with several examples. Our results show that the suggested TS decision model is better than a Quick Response （QR） decision model.

Adaptive algorithm for estimating excavation-Induced displacements using field performance data

Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of the model and(b)the uncertainties of the input variables.The uncertainties of these models can be characterized by a bias factor which is defined as the ratio of the actual displacement to the predicted displacement.The bias factors associated with the C&O method and the KJHH model are evaluated using the Bayesian method and a database of 71 excavations in Shanghai.To improve the predictions of the maximum displacement,an adaptive algorithm is proposed using field performance data.The performance of the proposed algorithm is demonstrated by an example in which excavation-induced displacements are generated by finite element method in normally consolidated clays.The example shows that the developed algorithm can significantly improve the predictions by incorporating the field performance data.

Consumers can learn and can forget-Modeling the dynamic decision procedure when watching TV

Facing the challenge of attracting consumers and winning market share under the pro-liferation of TV stations and channels,the traditional TV stations often make some mar-keting strategies.However,how to evaluate the effectiveness of different strategies and select the best one is a key issue.This study proposes to resolve this problem.We develop an innovative structural model to simulate the dynamic choices consumers make under two interactive behaviors:learning and forgetting.Learning behavior refers to updating programme quality assessment by using experience,while forgetting behavior prevents the use of previous experience.The Bayesian rules are employed to model learning behavior,and they are extended by incorporating an exponential decay function to mea-sure the effect of forgetting behavior.The structural model is tested and validated by using Hong Kong television viewing data.The empirical results show that when modeling consumer choice decisions,considering learning and forgetting behavior significantly improves the performance of the model in regard to rating prediction and marketing strategy evaluation.Five cases are simulated to show how the model is used to evaluate marketing strategies.Managerial implications are then discussed to guide the decision-making of traditional TV broadcasters and advertisers.