Comparison of two inverse analysis techniques for learning deep excavation response

Performance observation is a necessary part of the design and construction process in geotechnical engineering. For deep urban excavations, empirical and numerical methods are used to predict potential deformations and their impacts on surrounding structures. Two inverse analysis approaches are described and compared for an excavation project in downtown Chicago. The first approach is a parameter optimization approach based on genetic algorithm (GA). GA is a stochastic global search technique for optimizing an objective function with linear or non-linear constraints. The second approach, self-learning simulations (SelfSim), is an inverse analysis technique that combines finite element method, continuously evolving material models, and field measurements. The optimization based on genetic algorithm approach identifies material properties of an existing soil model, and SelfSim approach extracts the underlying soil behavior unconstrained by a specific assumption on soil constitutive behavior. The two inverse analysis approaches capture well lateral wall deflections and maximum surface settlements. The GA optimization approach tends to overpredict surface settlements at some distance from the excavation as it is constrained by a specific form of the material constitutive model (i.e. hardening soil model); while the surface settlements computed using SelfSim approach match the observed ones due to its ability to learn small strain non-linearity of soil implied in the measured settlements.     

Tectonic implication of geomorphometric analyses along the Saravan Fault: evidence of a difference in tectonic movements between the Sistan Suture Zone and Makran Mountain Belt

In this paper, remote sensing techniques,as well as field studies, have been used to investigate the geomorphological processes and landscape evolution along the Saravan Fault, SE Iran to highlight how topographic features were influenced by active tectonics. Quantitative geomorphic analysis was carried out using mountain-front sinuosity(Smf),valley floor width-valley height ratio(Vf), drainage basin asymmetry factor(Af), Hypsometric integral(Hi), drainage basin shape index(Bs), mean axial slope of channel(MASC), standard deviation of topography(STD) and index of active tectonic(Iat).Remote sensing techniques, as well as field studies revealed that the Saravan Fault have three parts trending N-S, NW-SE, and E-W. Obtained results show that basins with high Iat index are located at where the strike of the Saravan Faults changes and where several strike-slip faults are crossed the Saravan fault.