Publications

This paper presents a few comprehensive experimental studies for automated Structural Damage Detection (SDD) in extreme events using deep learning methods for processing 2D images. In the first study, a 152-layer Residual network (ResNet) is utilized to classify multiple classes in eight SDD tasks, which include identification of scene levels, damage levels, and material types. The proposed ResNet achieved high accuracy for each task while the positions of the damage are not identifiable. In the second study, the existing ResNet and a segmentation network (U-Net) are combined into a new pipeline, cascaded networks, for categorizing and locating structural damage. The results show that the accuracy of damage detection is significantly improved compared to only using a segmentation network. In the third and fourth studies, end-to-end networks are developed and tested as a new solution to directly detect cracks and spalling in the image collections of recent large earthquakes. One of the proposed networks can achieve an accuracy above 67 .6% for all tested images at various scales and resolutions, and shows its robustness for these human-free detection tasks. As a preliminary field study, we applied the proposed method to detect damage in a concrete structure that was tested to study its progressive collapse performance. The experiments indicate that these solutions for automatic detection of structural damage using deep learning methods are feasible and promising. The training datasets and codes will be made available for the public upon the publication of this paper.

This novel study provides new experimental evidence and a detailed comparative analysis of how various types of plastic materials influence concrete performance. Six widely used plastic materials were examined for their impact on the flexural strength of reinforced concrete (RC) beams, as well as the compressive strength, elastic modulus, and durability of concrete specimens. In the experimental program, 10% of the natural fine aggregate was replaced with particles of polyethylene terephthalate (PET), high-density polyethylene (HDPE), polyvinyl chloride (PVC), low-density polyethylene (LDPE), polypropylene (PP), and polystyrene (PS). A simplified life cycle assessment (LCA) model was included to compare the greenhouse gas emissions (measured as CO2-e) from managing plastic waste. The new experimental data indicate that, overall, incorporation of plastic waste materials into concrete has modest adverse effects, suggesting the viability of the resulting product as a sustainable material alternative. Flexural tests on RC beams showed that the addition of plastic particles has no adverse effects on flexural behavior under the specific test conditions. Furthermore, durability assessments using ultrasonic pulse velocity and electrical resistivity tests confirmed that plastic-modified concrete performs comparably to conventional mixes. LCA revealed that, with strategic improvements in recycling technology and logistics, using plastic waste in concrete can become an environmentally friendly option, helping to reduce the carbon footprint.

A new steel reinforcement system is introduced to be used in concrete columns. This new reinforcement, named Prefabricated Cage System (PCS), is an alternative to the rebar cage used in traditional reinforced concrete for faster, easier, and more reliable construction. PCS reinforcement is prefabricated off-site and then placed inside the formwork eliminating the time-consuming and costly labor associated with cutting, bending, and tying steel bars in traditional rebar construction. The axial strength, confinement, and displacement capacity of 15 small-scale column specimens reinforced with PCS and conventional rebar are experimentally investigated. The behavior of PCS specimens is evaluated and compared with that of similar rebar reinforced concrete columns. The effect of several parameters, such as steel tube thickness, opening dimensions, number and spacing of longitudinal and transverse steel, on the strength and displacement capacity is also investigated. Test results have shown that the axial load carrying capacity of specimens reinforced with PCS was similar to or better than that of reinforced concrete specimens. Axial load–displacement relations for the test columns are also predicted and compared with the measured response.

Robust Mask R-CNN (Mask Regional Convolutional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earthquakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High- resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.

Recent advances in technology allowed for the use of laser-based systems that can directly measure macrotexture properties of various surfaces. Volumetric or sand patch method has historically been used as the main technique for measuring macrotexture. Different available methods do not all measure the same surface properties and often generate different measurements. Thus, it is crucial to determine the most suitable method for measuring surface macrotexture. This paper investigates mean profile depth measurements from three laser based macrotexture measuring devices, including a laser profiler, a laser texture scanner and a circular texture meter. The results are compared with mean texture depth obtained from volumetric sand patch tests. Experiments were conducted to measure macrotexture of 26 laboratory specimens, which included asphalt and Portland cement concrete samples of various type and finish, as well as other common manufactured textured samples. Based on the evaluation of experimental data collected in this study, relationships are recommended to predict standard macrotexture using the mean profile depth data measured by a laser equipment or scanner.

This paper investigates and compares mean profile depth (MPD) measurements from two laser-based macrotexture measuring devices, namely a laser profiler and an Ames Laser Texture Scanner, to mean texture depth (MTD) results from volumetric sand patch tests. In addition, the effects of speed and material type on the MPD results for the profiler were also researched. The study used data obtained from field testing at three sites, each with a variety of pavement types, and laboratory testing on various types of Hot Mix Asphalt (HMA) and Portland cement concrete samples of varying finish, as well as other common, manufactured, textured samples. Analysis of the data showed that the MPD obtained from the Ames Laser Texture Scanner had the highest correlation to the MTD measurements determined using the sand patch test. It was also determined that the MPD values taken by the laser profiler decreased as the speed at which the sample was traveling increased. A new correlation for predicting MTD from laser profiler MPD was developed through laboratory testing. Additionally, it was found that material type had an effect on the laser MPD values.

Determining in-place concrete strengths is particularly important for the seismic evaluation of existing bridges and buildings. Underestimating the concrete strength in a seismic rehabilitation design by adopting a lower-bound estimate of the in-place concrete strength can undervalue component resistances, potentially triggering inappropriate rehabilitation solutions. The objective of this article is to review assessment requirements and guidelines and identify ways of setting core sampling requirements appropriate for seismic evaluation. Recommendations are based on a review of statistical methods to select sample size for an acceptable error level and assessment of outlier data.

The main objective of this study was to evaluate and improve the Ohio Department of Transportation's (ODOT's) current load rating procedures for corrugated metal culverts. This objective is achieved by testing 39 in-service culverts under static and dynamic loads, by evaluating the response of test culverts using available theoretical methods and numerical simulations, and by evaluating and advancing the current analysis tools and load rating methods based on the analytical and experimental evidence generated in this research. The experimental program was conducted to investigate the influence of several parameters on the field performance of culverts in Ohio. These parameters include backfill height, various static and dynamic load applications, and existing condition, size, shape, and other properties of corrugated metal culverts. Experimental results show that culvert deflections decrease nonlinearly with increasing backfill height. Deflections and strains were nearly zero in deep culverts with backfill height larger than 13 ft (4 m). Under static and dynamic truck loading, culvert deflections and strains increased significantly when the backfill height was less than about 6.5 ft (2.0 m). Responses of some of the test culverts were simulated using the two-dimensional finite element program CANDE. Deflections predicted from CANDE analysis were larger than deflections measured in the field. However, the moments and thrusts calculated from experimental strains were similar to those calculated using theoretical methods and CANDE. Experimental data and available theoretical studies were used to evaluate the current load rating methods. Recommendations are made to improve the analysis and evaluation procedures for corrugated metal culverts. Recommendations are based on an extensive review of load rating procedures and design practices, experimental data from 39 test culverts, and theoretical and numerical investigations. The recommended load rating procedures do not consider the effect of cover depth. New capacity reduction factors are introduced for culvert wall and seam, which require different appraisals for wall and seam during annual inspections. The authors recommend changes to rating factors for deep culverts and culverts subjected to low live load stresses.

This research is focused on modeling the behavior of reinforced concrete columns subjected to lateral loads. Deformations due to flexure, reinforcement slip, and shear are modeled individually using existing and new models. Columns are classified into five categories based on a comparison of their predicted shear and flexural strengths, and rules for combining the three deformation components are established based on the expected behavior of columns in each category. Shear failure in columns initially dominated by flexural response is considered through the use of a shear capacity model. The proposed model was tested on 37 columns from various experimental studies. In general, the model predicted the lateral deformation response envelope reasonably well.

Earthquakes and laboratory experience show that columns with inadequate transverse reinforcement are vulnerable to damage including shear and axial load failure. To study this behavior, four full-scale columns with light transverse reinforcement were tested quasistatically under unidirectional lateral load with either constant or varying axial loads. Test results show that responses of columns with nominally identical properties vary considerably with magnitude and history of axial and lateral loads. Observed behavior is compared with expected behavior based on available analytical models. The FEMA 356 assessment model predicted the column strengths well, but underestimated the displacements.

A new steel reinforcement system is introduced to be used in concrete columns. This new reinforcement, named Prefabricated Cage System (PCS), is an alternative to the rebar cage used in traditional reinforced concrete for faster, easier, and more reliable construction. PCS reinforcement is prefabricated off-site and then placed inside the formwork eliminating the time-consuming and costly labor associated with cutting, bending, and tying steel bars in traditional rebar construction. The axial strength, confinement, and displacement capacity of 15 small-scale column specimens reinforced with PCS and conventional rebar are experimentally investigated. The behavior of PCS specimens is evaluated and compared with that of similar rebar reinforced concrete columns. The effect of several parameters, such as steel tube thickness, opening dimensions, number and spacing of longitudinal and transverse steel, on the strength and displacement capacity is also investigated. Test results have shown that the axial load carrying capacity of specimens reinforced with PCS was similar to or better than that of reinforced concrete specimens. Axial load–displacement relations for the test columns are also predicted and compared with the measured response.

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Steel buildings are generally susceptible to the risk of progressive collapse in case one or a few load-carrying members are lost in an extreme event such as blast, impact, or fire. Thus, to avoid the catastrophic collapse of steel structures, it is imperative to investigate and learn from the performance of existing steel buildings for progressive collapse potential. The Ohio Union building, a multi-story steel-framed building that existed on the campus of Ohio State University, was tested earlier before its demolition by one of the co-authors for progressive collapse assessment under the successive removal of four columns located at the first-story level. The aim of this study is to conduct NLD (nonlinear dynamic) analysis for the building considering buckling of columns to numerically assess its potential for progressive collapse and then compare it with test observations. The calibrated finite element (FE) model was then extended to incorporate more hypothetical sequential and simultaneous column-loss scenarios. As one of the most widely accepted documents used among practicing engineers for numerical assessment of progressive collapse potential of buildings, the 2003 GSA (General Services Administration) guidelines were also applied. The simplified LS (linear static) analysis approach of the GSA guidelines was used for assessing the progressive collapse risk of the building, and the results were then compared with both test observations and NLD analysis. New dynamic increase factors (DIFs) are recommended for both force- and deformation-controlled actions to be used with the LS analysis.

The seismic design of super-tall buildings has become an important research topic in earthquake engineering. Limited research has been conducted on the distribution of plastic energy dissipation among the different components of super-tall buildings when subjected to strong earthquakes. A simplified two-dimensional (2D) nonlinear model is developed based on the analysis of Shanghai Tower, an actual super-tall building with a total height of 632 m. The accuracy of the simplified model is validated by comparing the results from modal analyses, and static and dynamic time-history analyses of the refined finite element model. Then, the proposed simplified model is used to determine the plastic energy dissipation of different components and the distribution of the total plastic energy dissipation over the height of the Shanghai Tower under different seismic intensities. The analysis indicates that the total plastic energy is mainly concentrated in the upper four Zones of the building and that the outrigger is the major plastic energy dissipation component in the Shanghai Tower.

Progressive collapse performance of steel structure with unreinforced masonry wall and load-bearing wall structure with concrete masonry unit (CMU) have been investigated through field experiments and computational simulations. During the experiments, single or multiple first-story columns or load bearing walls were physically removed from the buildings, which were demolished immediately after the experiments. Due to lack of full-scale building test data, the data produced in this research has been a valuable addition to the state of knowledge on gravity collapse of buildings. The primary goal of field experiments was to simulate the structural dynamic and static response of buildings that may experience collapse after sudden loss of column(s) or wall(s). Another objective was to investigate how the internal forces were redistributed within the building after each wall or column was removed. In this study, finite element models of the buildings were developed and analyzed to simulate the building performance and collapse potential. Computational models and simulations were examined and compared to the experimental data from the field tests. The contribution and effects of infill walls to progressive collapse resistance of frame structures were investigated. This study showed the robustness of existing buildings and the contribution of different structural components to collapse resistance.

In this research work, five different types of post-consumer plastics were mechanically ground into fine aggregate, and each type was used to prepare 2 in. (50 mm) mortar cubes by partial volumetric replacement of the sand. The purpose is to evaluate the effect of the plastic type and its shape on the density and the compressive strength of concrete. The plastic products used in this study are usually not collected by curbside recycling facilities and are discarded in landfills or incinerated. The different types of plastics investigated were Polyethylene terephthalate (PET), High-Density Polyethylene (HDPE), Polypropylene (PP), Polystyrene (PS), and Acrylonitrile Butadiene Styrene (ABS). A total of 180 cubes with 5%, 10%, and 15% replacement were prepared and tested for their densities at the age of 28 days and their compressive strengths at the ages of 7 and 28 days. This work concluded by proposing general equations to predict the reduction in the density and compressive strength of the mortar with the increment in the plastic replacement.