Almost all historical minarets in Turkey were constructed using cut stone, masonry blocks or combination of these two materials. The structural and geometrical properties of each masonry minaret, or slender tower structure, depend on many factors including the structural knowledge and applications at the time of construction, experience of the architect or engineer, seismicity of the region, and availability of construction materials in that area. Recent earthquakes in Turkey have shown that most masonry minarets in high seismic regions are vulnerable to structural damage and collapse. In this study, in order to investigate the dynamic behavior of historical unreinforced masonry minarets, three representative minarets with 20, 25, and 30 m height were modeled and analyzed using two ground motions recorded during the 1999 Kocaeli and Duzce, Turkey earthquakes. The modal analyses of the models have shown that the structural periods and the overall structural response are influenced by the minaret height and spectral characteristics of the input motion. The dynamic displacement and axial stress time histories are computed at the critical points on the minarets. During recent earthquakes, most minaret failures occurred above the base of the structure. Consistent with the observed response, the largest stresses were calculated at the same location.
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.
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.
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.
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.
No abstract is provided for this article.
Before the introduction of special requirements in the 1970s, reinforced concrete building frames constructed in zones of high seismicity in the US had details and proportions similar to frames designed solely for gravity loads. Columns generally were not designed to have strengths exceeding beam strengths, so column failure mechanisms often prevail. Relatively wide spacing of transverse reinforcement was common, such that column failures may involve some form of shear or flexure-shear failure, in some cases followed by loss of axial load capacity. This study examines laboratory behavior of columns with light transverse reinforcement and proposes models for shear strength and subsequent axial load failure that may be suitable for evaluation of existing building frames.
Modern society is demanding that the use of energy associated with construction and operation of structures be investigated during the planning and design phases. The engineering community has been striving to design more sustainable buildings in an attempt to reduce both raw material requirements and energy use during all phases of design. Structural engineers currently have very limited guidance on how to incorporate sustainability concepts in their designs. Innovative methods are needed to address the environmental impact, energy use, and other sustainability issues faced during planning and design of buildings. This paper investigates and discusses five sustainable structural design methodologies: Minimizing Material Use, Minimizing Material Production Energy, Minimizing Embodied Energy, Life-Cycle Analysis/Inventory/Assessment, and Maximizing Structural System Reuse. The goal of this paper is to describe and address issues associated with the proposed design methodologies to determine which, if any, can produce the most sustainable structural designs. It was determined that no single methodology can address all the issues surrounding sustainable structural design. Also, it was determined that combinations of two or more methodologies may increase the ability of design professionals to produce more sustainable designs.
Silos are special structures subjected to many different unconventional loading conditions, which result in unusual failure modes. Failure of a silo can be devastating as it can result in loss of the container, contamination of the material it contains, loss of material, cleanup, replacement costs, environmental damage, and possible injury or loss of life. Silo damage and failures that occurred in different regions of the world are presented in the paper using illustrative photos. Also provided are a review and discussion of the common or spectacular silo failures due to explosion and bursting, asymmetrical loads created during filling or discharging, large and nonuniform soil pressure, corrosion of metal silos, deterioration of concrete silos due to silage acids, internal structural collapse, and thermal ratcheting. Silo damage and failures from several earthquakes are also presented.
The 6 February 2023 Kahramanmaraş earthquakes in Turkiye, which measured 7.8 and 7.5 moment magnitude ( M w ) per the United States Geological Survey (USGS), affected 13 provinces and over 15 million people, according to the Turkish Government. The Earthquake Engineering Research Institute’s (EERI) Buildings Reconnaissance Team visited the populated centers as well as small towns in Turkiye that were most affected by these earthquakes. The team focused on understanding the overall structural performance of buildings, including correlation with maximum spectral acceleration and peak ground velocity at nearby ground motion recording stations. This article discusses the vulnerability of concrete buildings, which constitute most of the building stock in the region, performing an overall assessment of structural systems as well as components. More than 160 individual buildings at about 130 sites were observed. The construction period of these buildings varied from pre-2000s to as new as a few years old. Turkish building codes underwent significant changes after the 1999 Kocaeli earthquake and most recently in 2018. The findings in this article include not only the behavior of critical gravity and lateral structural elements but also the participation of non-structural elements in the seismic response of the structure, such as infills, non–load-bearing partitions, and perimeter unreinforced masonry infill walls. This article discusses the findings from field observations related to design, detailing, and construction practices. Findings illustrating the seismic performance of building systems and individual components such as floor slabs, beams, columns, shear walls, and foundations, with key takeaways to improve the seismic design guidelines, construction, and inspection practices, are summarized.
No abstract is provided for this article.
A new reinforcement system, Prefabricated Cage System (PCS), is proposed to perform the function of longitudinal and transverse steel in reinforced concrete members. PCS is made from a solid steel tube or plate acting as transverse and longitudinal steel connected monolithically. PCS reinforcement eliminates some of the weaknesses and detailing problems inherent in traditional rebar reinforced concrete construction resulting in easier, more reliable, and faster construction. The confinement provided by PCS is investigated by comparing the results from 6 small-scale column tests. The specimens were tested by axially loading the concrete core. The effects of PCS tube thickness, and the width and height of transverse and longitudinal steel on the provided confinement and displacement capacity are investigated. Test results show that PCS provides higher confinement capacity than similar rebar reinforcement.
Robust Mask R-CNN (Mask Regional Convolu-tional 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 earth-quakes. 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.
