Dr. Khoshnevisan's Research

  • Liquefaction often causes damage to infrastructure such as buildings, bridges, and lifelines. Liquefaction-induced ground movements such as settlement and lateral spread are of major concern to engineers who have to evaluate seismic risk. Research is conducted to evaluate liquefaction-induced settlement and lateral spread using a cone penetration test (CPT). Existing CPT-based models often overestimate liquefaction-induced settlement and lateral spread. A database of case histories of settlement in recent earthquakes is compiled and used to calibrate the model bias of a CPT-based model, from which a simplified procedure is developed that allows for estimation of the probability of exceeding a specified settlement and lateral spread at a given site. 
  • While the maximum likelihood method is increasingly used by researchers in liquefaction analysis and other fields in geotechnical engineering, there is no easy-to-access publication to elucidate how this method works and how it can be used efficiently. This missing element hinders the wider use of the maximum likelihood method in the geotechnical profession. Hence, a chapter is provided to introduce the maximum likelihood principle with an emphasis on its application in geotechnical engineering,
    and to exemplify its use in the development of various probabilistic models for liquefaction probability prediction.


​Liquefaction is a phenomenon whereby a fully or partially saturated soil loses strength due to an applied stress. Liquefaction-induced ground movements are a major cause of damage to buildings, bridges, and lifelines.
  • It is vital to evaluate and validate the site condition database and liquefaction quantification earthquake models and update them to better estimate earthquake-related losses. Research is conducted to identify and fill in gaps in current site condition data and provide validated data for earthquake models. The objective is to evaluate and validate site condition database for U.S. earthquake hazard analysis, and to develop high resolution database of site condition (quantified by Vs30) in U.S. within the selected locations
  • Probabilistic Models of Assessing Liquefaction-induced Settlement
  • Probabilistic Models of Assessing Liquefaction-induced Lateral Displacement
  • Maximum Likelihood Principle and Its Application in Soil Liquefaction Assessment
  • Customized Site Amplification Database and Probabilistic Hi-Resolution Liquefaction Hazard Model
  • Probabilistic Performance-Based Liquefaction Risk Assessment and Mitigation Countermeasure
  • Experimental and numerical studies of the effect of interlayerings on soil liquefaction 

Robustness to Lack of Knowledge

In general, the design robustness is achieved if the system response is insensitive to the variation in the uncertain input parameters (called “noise factors”). In other words, a design is considered robust if the system response exhibits little variation, even though there is high variation in the input parameters. Robust design achieves this desirable outcome by carefully adjusting ‘design parameters’ (i.e., the parameters that can be controlled by the designer, such as the geometry and dimensions) without reducing the uncertainty in the noise factors.
  • Developed practical methods for robust design of geotechnical systems
  • Developed a new Robustness measure
  • Developed a new knee point selection method
  • Applied the developed Robust Geotechnical Design approach to many geotechnical problems such as shallow foundations, drilled shafts, earth and rock slopes, and braced excavations.
  • Used Robust Geotechnical Design approach for assessing the characteristic value selection methods for design with LRFD
  • Robust design of geothermal foundations
  • Robust design of infrastructures with risk consideration

Big Data Analytics & Machine Learning

  • Machine learning techniques for robustness and resiliency
  • Risk-informed decision

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Cincinnati OH 45221-0071


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