Publications

A new concrete confinement model is developed to predict the axial load versus displacement behavior of circular columns under concentric axial load. The new confinement model is proposed for concrete filled steel tube columns as well as circular reinforced concrete columns with steel tube jacketing. Existing confinement models were evaluated and improved using available experimental data from different sets of columns tested under similar loading conditions. The proposed model is based on commonly used confinement models with an emphasis on modifying the effective confining pressure coefficient utilizing the strength of the unconfined concrete and the steel tube, the length of the column, and the thickness of the steel tube. The proposed model predicts the ultimate axial strength and the corresponding strain with an acceptable degree of accuracy while also highlighting the importance of the manner in which the steel tube is used.

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, material types, etc. 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.

A large number of reinforced concrete (RC) buildings and bridges is deemed structurally deficient. This is either because the infrastructure continues to age and deteriorate or the strength or deformation capacity of the existing older infrastructure does not meet the current code requirements, e.g., in high seismic regions. Thus, the need for more efficient retrofit methods has increased in recent years. Currently, there are only a few methods used for strengthening or retrofitting columns. Steel jackets and Fiber Reinforced Polymer (FRJP) composites are the two most commonly used methods. In this study, along with these two retrofit methods, concrete jackets reinforced with spiral rebar, Welded Wire Fabric (WWF), and a new steel reinforcement termed PCS are investigated under different axial load conditions.

This study presents a new vision-based deep learning method to monitor and evaluate the structural health of in-service infrastructure. For this purpose, three different camera placements, including remote, structure-mounted, and drone-mounted cameras, are proposed to capture the vibrations or displacements of bridges. The vision-based deep learning method is verified by an optical flow approach. Various techniques, such as visual data denoising and camera motion removal, are utilized to process the test data for displacement measurements and extract the structural frequencies. Structural models of bridges are analyzed to validate the measurements and assess the structural health of several pedestrian, traffic, and railway bridges without interfering with traffic. Measurements in the field experiments and results from the structural analysis on tested bridges show that the proposed framework works successfully and can be potentially engineered to monitor the structural health of existing bridges.

As part of an experimental program, a large number of in-service culverts were tested to investigate the influence of several parameters on field performance of culverts. These parameters included cover depth, load application, size, shape, and other properties of corrugated metal culverts. Response of 14 test culverts was simulated using a two-dimensional finite element program and a three-dimensional finite difference program. Deflections predicted from the two-dimensional analyses were larger than the deflections measured in the field. However, the experimental and calculated thrust forces were similar. Deflections and thrust forces predicted from the three-dimensional analysis were similar to the experimental results. The critical factors affecting the response of the culverts include cover depth, culvert size, metal thickness, and elastic modulus and other properties of backfill soil. The influence of these parameters on the culvert behavior is investigated through numerical simulations using the established modeling properties.

No abstract is provided for this article.

Vertical collapse performance of existing multi-story buildings has been investigated through field experiments and computational simulations. Four reinforced concrete (RC), two masonry, and three steel frame buildings have been tested in recent years. During the experiments, single or multiple first-story columns 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 main 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) due to, say column shear failure in a reinforced concrete building. Another objective was to investigate how the internal forces are redistributed within the building after each column is removed. In this study, two- and three-dimensional models of the buildings were used to simulate the building performance and collapse potential. Computational models and simulations were examined and compared to the experimental data from the field tests. It is noted that the current nonlinear modeling and progressive collapse assessment guidelines (GSA 2013) use nonlinear analysis parameters found in the seismic assessment of existing buildings (ASCE/SEI 41, 2013), e.g., performance limit states and m-factors to evaluate nonlinear performance. This study showed robustness of existing buildings and contribution of different structural components to collapse resistance.

The internal forces increase in structural components neighbouring a member, usually a column, removed or destroyed as a result of manmade or natural hazards, such as an explosion. If the additional internal forces created by an initial small or local structural failure can not be efficiently redistributed within the structure, damage spreads and partial or total collapse (progressive collapse) of the building occurs. Progressive collapse has been of an increasing concern in the structural engineering community, especially since the collapse of the World Trade Center towers in 2001. The terrorist attacks showed that well-designed and robust modern buildings can be susceptible to progressive collapse. The Ronan Point collapse of 1968 in London, and the bombing of the Alfred P. Murrah building in Oklahoma City in 1996 are the most publicized examples of progressive collapse [1]. Therefore, it is necessary to design of buildings to resist progressive collapse. In this study, progressive collapse performance of an actual building was investigated. The Ohio Student Union building, located on the Ohio State University campus, was also tested by physically removing four first story columns from one of the long perimeter frames prior to building's scheduled demolition. Our field experiments and analytical studies provide both practical and fundamental information on the collapse response of an existing building with a regular structural configuration. A commercially available computer program, SAP2000 was used to model and analyze the building following the General Services Administration (GSA, 2003) guidelines. The elastic static and nonlinear dynamic analysis results are presented and their implications are discussed.

Reinforced concrete (RC) structures in low to moderate seismic regions and many older RC structures in high seismic regions include columns with steel reinforcement details not meeting the requirements of modern seismic design codes. These columns typically fail in shear or in a brittle manner and their behavior must be accurately captured when RC structures are modeled and analyzed. The total lateral displacement of a low ductility or shear critical RC column can be represented as the sum of three displacement components: (1) flexural displacement, (2) displacement due to slippage of the reinforcing bars at column ends, and (3) shear displacement. In this study, these three displacement components are separately modeled and then combined together following a proposed procedure based on the expected overall behavior of the column and its failure mechanism. A simplified slip model is proposed. The main objective of this research is to develop an easy-to-apply method to model and capture the cyclic behavior of RC columns considering the shear failure mechanism. The proposed model is validated using the available data from RC column and frame experiments.

This study investigates the field performance of culvert joints in five existing pipe culverts. Test culverts consisted of two corrugated metal culverts, two reinforced concrete culverts, and one high-density polyethylene culvert. Culverts were selected for backfill depths less than 1.5 m and inner diameters ranging from 0.9 to 2.1 m. Static loading was applied to the culverts by parking a heavily loaded truck at different positions above the culvert joint. Dynamic loading was applied by driving the truck over the culvert at speeds ranging from 8 to 48  km/h. Culvert deflections and strains were measured near one of the culvert’s joints. Separation at the joint was also measured. The results indicate that separation at the joint was quite significant at times. In the worst case, the measured separation at the joint was equal to 50% of the measured deflection. Strains measured near the joint in steel culverts were significantly smaller than the yield strain. The dynamic deflections were typically 10–40% less than the static deflections.

Historical, religious and monumental structures and their susceptibility to damage in recent earthqaukes in Turkey are presented and discussed in this chapter. Turkey has a very large number of historical structures and is located in one of the most seismically active regions of the world. Some of these historical and monumental masonry and reinforced concrete structures suffered substantial damage or collapsed during two major earthquakes in 1999. The Kocaeli (Mw7.4) and Düzce (Mw7.2) earthquakes occurred on August 17 and November 12, 1999, and ruptured approximately 110 km and 40 km of the 1550-km-long North Anatolian fault, respectively. This chapter describes briefly the construction materials and techniques for historical religious and monumental structures and state-of-practice in Turkey, and presents dynamic analyses of a masonry minaret example. The seismic performance of the mosques and minarets (tall slender towers) during the 1999 earthquakes is presented.

A new reinforcement system termed the prefabricated cage system (PCS) that can be used as an alternative to the rebar reinforcement cage is economically evaluated. PCS is a prefabricated reinforcement that enables easier, faster, and more reliable construction. Use of PCS shortens the construction schedule time and lowers total construction cost. This is important to both owners and construction contractors. The engineering economics methods presented in this paper would also be of interest to researchers. Reinforced concrete structures with PCS reinforced columns have been considered in this research, as it is one of the major applications of PCS. Various parameters affecting the economics of PCS are reviewed and a case study structure is analyzed comparing the costs of the structure with rebar reinforced columns to costs of the structure with PCS reinforced columns. The investigation shows that using PCS results in a 33.3% time savings and a 7.1% cost savings over rebar for each column. This results in an average of 3.6% savings on total project cost; an average of 22.2% savings on total column costs; 20.4% savings on total project time period, and 33.3% savings on columns construction time period. The cost savings are estimated based on production of small quantities of PCS reinforcement. Mass production of PCS reinforcement would result in even higher cost savings.

Earthquake reconnaissance has identified failure of reinforced concrete columns as a primarycause of collapse of older existing reinforced concrete building frames during earthquakes.Apparent column failure, however, does not always result in building collapse. A study of columns tested in the laboratory examines loss of lateral and vertical load capacities.Correlations with geometric, materials, and loading characteristics are identified.

To investigate the size and slenderness effect on the axial-compressive behavior of basalt fiber–reinforced polymer (BFRP)-confined predamaged concrete, five groups of concrete cylinders with different sizes and slenderness ratios were designed and tested. The cylinders were axially preloaded to three predamage levels, then repaired using BFRP, and reloaded. The results showed that the concrete predamage had an adverse effect on the ultimate strength and initial elastic modulus of BFRP-confined concrete. Except for the smallest specimens affected by the wall effect, the initial analysis found that the ultimate strength of BFRP-confined concrete decreased with an increase in size and slenderness ratio, and the size and slenderness effect decreased with an increase in BFRP confining pressure, while these increased with the severity of concrete predamage. However, there was no obvious size or slenderness effect on the ultimate strain of BFRP-confined concrete. Through multifactorial analysis, it was confirmed that the ultimate strength of BFRP-confined undamaged and predamaged concrete was influenced by the slenderness. Considering the effect of size, slenderness, and predamage, monotonic and cyclic models were developed for BFRP-confined concrete. Finally, a uniaxial material object was added into OpenSees to provide an effective numerical material model for theoretical analyses and engineering applications.

A large number of corrugated metal pipe-arch culverts are located under highways. This study investigates the field performance of four existing pipe-arch culverts under static and dynamic loads. Effects of various parameters were considered in selection of the culverts, including backfill height, loading conditions, age of placement, and culvert geometry. Static loads were applied at ten different locations above each culvert using heavily loaded test trucks. Six dynamic tests were conducted at speeds varying from 8 to 64km/h . A portable instrumentation frame was installed inside each test culvert to monitor the deflections at five critical locations. During each test, strains were also measured using 14 strain gauges. Test results indicated that culvert response was influenced significantly by the backfill height. Nearly symmetrical deflection patterns were recorded for symmetrical loading about the longitudinal vertical plane through the crown. The maximum static deflections were larger than the maximum dynamic deflections for each culvert.