Rapid visual screening of soft-story buildings from street view images using deep learning classification

Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.

A method for automated development of model and fragility inventories of nonductile reinforced concrete buildings

The nonductile reinforced concrete building(NDRCB)stock-typically,pre-1974 structures in the U.S.-is a well-known high-risk group for seismic hazards.Prior studies indicate that there are approximately 1500 NDRCBs in Los Angeles.Through various ordinances,the owners are currently required to choose between demolition and,when appropriate,seismic retrofitting.Because fulfilling these ordinances will take decades,the potential risk of major losses will persist.In this study,a method for automated development of structural analysis models and damage fragilities for non-ductile moment-resisting frames is established.This capability enables seismic risk assessment at a regional scale using relatively limited building metadata and the era-specific seismic design code.The approach is used first to develop archetypal models in OpenSees,verified through static pushover and nonlinear time-history analyses against prior detailed studies.Fragility curves for discrete damage states are developed through a probabilistic seismic demand model.Additional investigations are carried out to consider the influence of soil-structural interaction effects and to determine the most suitable seismic intensity measures to quantify the seismic damage levels of NDRCB frames.The sensitivity of the proposed modeling method to variations/uncertainties in building configurations and properties is also examined through parametric studies.The method is limited to a particular subcategory of NDRBCs-namely,moment-resisting frames-but extensions to other types appear straightforward.

An enhanced damage plasticity model for predicting the cyclic behavior of plain concrete under multiaxial loading conditions

Some of the current concrete damage plasticity models in the literature employ a single damage variable for both the tension and compression regimes,while a few more advanced models employ two damage variables.Models with a single variable have an inherent dificulty in accounting for the damage accrued due to tensile and compressive actions in appropriately different manners,and their mutual dependencies.In the current models that adopt two damage variables,the independence of these damage variables during cyclic loading results in the failure to capture the effects of tensile damage on the compressive behavior of concrete and vice-versa.This study presents a cyclic model established by extending an existing monotonic constitutive model.The model describes the cyclic behavior of concrete under multiaxial loading conditions and considers the influence of tensile/compressive damage on the compressive/tensile response.The proposed model,dubbed the enhanced concrete damage plasticity model(ECDPM),is an extension of an existing model that combines the theories of classical plasticity and continuum damage mechanics.Unlike most prior studies on models in the same category,the performance of the proposed ECDPM is evaluated using experimental data on concrete specimens at the material level obtained under cyclic multiaxial loading conditions including uniaxial tension and confined compression.The performance of the model is observed to be satisfactory.Furthermore,the superiority of ECDPM over three previously proposed constitutive models is demonstrated through comparisons with the results of a uniaxial tension-compression test and a virtual test.

Local impact factor assessment for deck slabs of steel–concrete composite bridge system with twin girders

The steel-concrete composite bridge system with twin girders,referred to as a steel plate composite girder bridge,is widely adopted for short-to medium-span highway bridges due to its ability to enable rapid prefabrication and construction in bridge engineering.Considering the structural design of steel plate composite girder bridges,which are wide but shallow in depth,their deck slabs are vulnerable to vertical impacts from vehicle loads.Structural performance may be negatively affected by excessive dynamic displacement of deck slabs.It is difficult to assess the dynamic response of the deck slabs by existing methods,since traditional specifications only use a global impact factor to describe the dynamic effect of moving vehicles on the bridge as a whole,regardless of the local dynamic effect on the deck slabs.Therefore,this study aims to assess the local dynamic effect of moving vehicles on the deck slabs of steel plate composite beam bridges using field tests and finite-element methods.A systematic approach was employed to analyze parameters influencing bridge-vehicle interaction.Additionally,an improved method was presented to calculate the local impact factor and parametric studies were discussed.The findings indicated that the local impact factor of deck slabs is significantly greater than the global impact factor.Road surface roughness is the most significant parameter affecting deck slab dynamic behavior.