A beam on elastic foundation method for predicting deflection of braced excavations considering uncertainties

• Published in: International journal for numerical and analytical methods in geomechanics Vol. 47; no. 4; pp. 533 - 548
• Main Authors: Tang, Cong, He, Shu‐Yu, Zhou, Wan‐Huan
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.03.2023
• Subjects: Algorithms; Bayesian analysis; Bayesian probabilistic method; Bayesian theory; beam on elastic foundation; braced excavations; Conditional probability; Construction; Damage prevention; Deflection; Elastic foundations; Frameworks; Mathematical models; Methods; Parameter uncertainty; Parameters; Predictions; Probability theory; Process parameters; Soil resistance; Statistical analysis; uncertainty; wall deflection
• ISSN: 0363-9061; 1096-9853
• DOI: 10.1002/nag.3480

Predicting wall deflection is important to provide a critical reference to evaluate the current construction conditions and prevent potential damage risks of adjacent facilities during excavations. This paper presents a combination of a beam on elastic foundation model (BEFM) and the Bayesian framework to realize effective probabilistic predictions of wall deflection at various depths in braced excavations. First, a finite element solving algorithm to calculate wall deflection for the BEFM is developed and incorporated into the Bayesian framework. Next, the most suitable distribution pattern for soil resistance and an appropriate set of uncertain parameters in the BEFM are determined through the application of the Bayesian model selection technique. Meanwhile, the uncertain parameters are updated. A prediction is then made using the optimal model and corresponding posterior probability distributions of the updated parameters at each stage. The parameter updating and prediction process are repeated as additional field observations become available during construction. The performance of the proposed method is examined using a field case study. The results show that this method provides a satisfactory approach to predict both the magnitudes and profile of deflection when considering uncertainties. Additionally, comparisons with a Bayesian updating framework using a surrogate model (i.e., the KJHH model) indicate higher updating efficiency of the developed method.