Automated Damage Index Estimation of Reinforced Concrete Columns for Post-Earthquake Evaluations

In emergency scenarios, immediate reconnaissance efforts are necessary. These efforts often take months to complete in full. While underway, building occupants are unable to return to their homes/businesses, and thus, the impact on the society of the disaster-stricken region is increased. In order to mitigate the impact, researchers have focused on creating a more efficient means of assessing the condition of buildings in the post-disaster state. In this paper, a machine vision-based methodology for real-time post-earthquake safety assessment is presented. A novel method of retrieving spalled properties on reinforced concrete (RC) columns in RC frame buildings using image data is presented. In this method, the spalled region is detected using a local entropy-based approach. Following this, the depth properties are retrieved using contextual information pertaining to the amount and type of reinforcement which is exposed. The method is validated using a dataset of damaged RC column images.This material is based in part upon work supported by the National Science Foundation under Grant Numbers CMMI-1034845 and CMMI-0738417.This is the accepted manuscript. The final version is available from ASCE at http://dx.doi.org/10.1061/(ASCE)ST.1943-541X.000120

Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations

Concrete cracking is a ubiquitous phenomenon, present in all types of concrete structures. Identifying and tracking the amount and severity of cracking is paramount to evaluating the current condition and predicting the future service life of a concrete asset. Concrete cracks can indicate reinforcement corrosion, the development of spalls or changing support conditions. Therefore, monitoring cracks during the life span of concrete structures has been an effective technique to evaluate the level of safety and preparing plans for future appropriate rehabilitation. One growing technique are unmanned inspections using Unmanned Aerial Vehicles (UAV). UAVs are drones equipped with cameras, sensors, GPS, etc. RGB images (color images in Red, Green and Blue color space) are obtained from a camera mounted on a UAV flying around the structure, to detect cracks and other defects. Each image captured by UAV needs to be evaluated to track the crack formations. To save time, this task can be done by applying image processing techniques to automatically detect and report cracks rather than using a human to identify them. In addition, processing RGB images with sufficient information, such as the distance of camera to surface for each picture, will provide the dimension of the cracks (length and width). The report consists of the following sections: A literature review of image processing techniques used in structural health monitoring and other fields of interest is provided in chapter 2. The Proposed method to identify cracks is demonstrated in Chapter 3. Experimental results, conclusion and future work are presented in Chapter 4. Appendix A includes the processed images using the proposed method and Appendix B includes the comparison between Talab’s method and the proposed method. In Appendix C, a “readme” file is given to run the program, and finally Appendix D shows the Matlab Code

Image-based Automated Width Measurement of Surface Cracking

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions

STR-906: COMPUTER-IMAGE-BASED LOOSENED BOLT DETECTION USING SUPPORT VECTOR MACHINES

Despite many contact-sensor-based methods have been proposed to monitor and detect structural defects, there are still difficulties compensating for environmental effects and malfunctions of attached sensors, which are main reasons for transmitting false signals. Moreover, regardless of releasing correct or incorrect signals, it eventually leads to human-conducted on-site inspections. In light of these shortcomings, vision-based inspections are considered as potential approach to overcome the explained issues. A number of vision-based methods for detecting damages from images have been developed. However, there are only a few vision-based methods for detecting loosened bolts. Thus, a computer-vision method for detecting loosened bolts is proposed. This study includes two algorithms. The first one is a preprocessing to crop bolt images from bolted-joint images. The second algorithm is a feature extraction by integrating previously proposed algorithms in computer-vision. To accomplish an automated inspection, linear support vector machine is trained and used as a classifier. The robustness of the proposed is verified by the experimental validation; 22 bolt images are used to build a classifier, and 40 bolt images are tested

Detecting and Evaluating Cracks on Aging Concrete Members with Deep Convolutional Neural Networks

Cracks in concrete structures are evaluated through a timely and subjective manual inspection. The location of cracks is often recorded in an inspection report where some cracks are measured. Although measurements or locations may not be necessary for all cracks observed in concrete members, if quantitative data can be gathered in an autonomous way, allowing measurement data to be used in tracking changes in spatial and temporal scales, this quantitative data can provide useful information not yet captured in the manual inspection process. This thesis aims to construct an image-based crack detection and evaluation pipeline that can assist health monitoring of aging concrete structures, by providing crack locations and measured crack properties for the entire structural member. Over 16,000 images of aging concrete bridge deck were collected from cameras attached on an unmanned aerial vehicle, machine vision cameras attached on a ground vehicle, and other literature. Mask and Region based Convolutional Neural Network (Mask R-CNN) was utilized to train 256 by 256-pixel patches of collected images using three distinct training strategies to detect and segment concrete cracks on bridge decks. Resulting crack masks were translated into binary data (crack or non-crack pixels) and skeletons of the mask were created where the Euclidean distance from the center of the skeleton to the edge of the mask were measured. This allowed to calculate the relative crack width, length, and orientation of each detected crack. Relative crack properties were transformed into real-world unites using the ground sampling distance of the host image. Image patches were then compiled to construct a crack map of the entire structural member. A case study was conducted on the deck and pier of an aging concrete bridge to test the robustness of the proposed data pipeline. The study yielded that the model was able to successfully detect cracks with an average width of 0.020 inches and were able to make accurate measurements of crack widths that are larger than 0.080 inches. In order to improve the measurements for smaller crack widths, the ground sampling distance needs to be to the scale of the crack width in interest. The image-based data pipeline developed in this study demonstrates potential for the application in autonomous inspections of concrete members. In addition, the data pipeline can be used as a reference framework to provide an example on how computer-vision based data analytics can provide useful information for structural inspections of aging concrete members. Advisor: Chungwook Si

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research

Efficient Evaluation of the Number of False Alarm Criterion

This paper proposes a method for computing efficiently the significance of a parametric pattern inside a binary image. On the one hand, a-contrario strategies avoid the user involvement for tuning detection thresholds, and allow one to account fairly for different pattern sizes. On the other hand, a-contrario criteria become intractable when the pattern complexity in terms of parametrization increases. In this work, we introduce a strategy which relies on the use of a cumulative space of reduced dimensionality, derived from the coupling of a classic (Hough) cumulative space with an integral histogram trick. This space allows us to store partial computations which are required by the a-contrario criterion, and to evaluate the significance with a lower computational cost than by following a straightforward approach. The method is illustrated on synthetic examples on patterns with various parametrizations up to five dimensions. In order to demonstrate how to apply this generic concept in a real scenario, we consider a difficult crack detection task in still images, which has been addressed in the literature with various local and global detection strategies. We model cracks as bounded segments, detected by the proposed a-contrario criterion, which allow us to introduce additional spatial constraints based on their relative alignment. On this application, the proposed strategy yields state-of the-art results, and underlines its potential for handling complex pattern detection tasks

A Novel Bridge Information Modeling (BrIM) Based Framework for Bridge Inspections

Bridges are critical components of civil infrastructure. According to the National Bridge Inventory (NBI), there are more than ten thousand structurally deficient bridges in the United States. It becomes critical for the authorities to maintain their serviceability and reliability in order to keep the transportation system operational. Current bridge condition assessment practices are mainly based on visual inspections carried out by technical experts, which is subjective as observations and opinions may vary from one individual to another, and is expensive and prone to human errors. The main focus of this study is to help improve current inspection practices by implementing image processing algorithms to detect concrete surface cracks and integrate the results into a bridge information modeling (BrIM) based framework. Integrating crack detection algorithm results with BrIM will allow users to view and explore cracks and their properties linked to a three dimensional (3D) model of the inspected bridge component. The proposed methodology processes 2D images by adjusting pixel parameters of gray scale images and detects cracks with their dimensional aspects. It implements existing crack detection algorithms, a scaling tool to automatically measure crack dimensions, and includes a framework to integrate crack detection results with BrIM for inspecting bridges in a more efficient manner. This will enable more effective repairs and maintenance operations, saving a considerable amount of effort, time, and money

초고성능 콘크리트의 다기능 복합 응용을 위한 탄소나노튜브의 적용 및 영향 분석

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 건축학과, 2020. 8. 홍성걸.This study aimed to develop multi-functional ultra-high performance concrete (UHPC) with excellent mechanical strength, electromagnetic interference (EMI) shielding effectiveness (SE), and damage sensing capabilities for applying structural health monitoring (SHM), in on-site production. Carbon nanotubes (CNTs), selected as a key material to achieve the purpose of this study, were mixed with UHPC and investigated with regard to dispersion methods, mechanical properties, EMI SE, damage sensing, electrical curing (EC) and structural modifications with respect to CNT incorporation. Ozone treatment was applied to CNTs as a dispersion method and its impact on dispersion of CNTs and hydration reaction of UHPC were investigated. The results reveals that oxygenic and carboxylic groups, formulated by ozone treatment, functionalized the surface of the CNTs and enveloped the cementitious grains, which increasing the degree of dispersion of CNTs and interfacial interaction between CNTs and UHPC particles. Ozone treatment provided multiple nucleation sites and double steric repulsion, accelerating hydration at early ages and improving compressive strength at later ages. Thus, the proposed ozone treatment can be an effective way to disperse CNTs in UHPC. Dispersed CNT suspensions were fabricated using sonication and subsequent shear mixing with superplasticizer, a proposed method for dispersing high content of CNTs in UHPC. Then, the CNT suspensions were incorporated into UHPC to form UHPC/CNT composites and their mechanical properties were investigated with respect to the CNT content. The proposed dispersion method effectively dispersed CNTs within both an aqueous solution and UHPC composite up to the critical incorporation concentration (CIC). In addition, it was found that CNT content below the CIC improve the mechanical properties of UHPC through pore filling, bridging, and calcium-silicate-hydrates (C-S-H) stiffening, whereas CNT content above CIC weakens the properties due to agglomeration of CNTs, suppression of hydration, and increase in air-voids. A dispersed CNTs remarkably improved the electrical conductivity and resulting SE of UHPC up to the percolation threshold. Two testing methods for EMI SE (ASTM D4935-18 and IEEE-STD-299) verified the result and suggested that ASTM D4935-18 can only be used to evaluate a rough trend of SE, and IEEE-STD-299 using the samples with sufficient incident area size at least 1200 × 1200 mm2 should be considered to accurately evaluate the EMI SE at actual structure level. In addition, a practical model to effectively estimate the SE of UHPC was proposed based on statistical analysis of the experimental results. The addition of CNTs significantly decreased electrical resistivity of the UHPC, enabling EC at low voltages in the range of 19–23 V; improved mechanical properties via bridging, pore filling, and C-S-H stiffening effects; and influenced the deflection hardening behavior under flexural stress. Furthermore, the UHPC/CNT under compressive or flexural stress exhibited significant crack sensing capabilities due to the obtained low resistivity. A dramatic fractional change in resistivity (FCR) value of the UHPC/CNT composites can represent the failure under compression or first cracking under flexure. Therefore, it was verified that the UHPC/CNT can extend the applications of UHPC especially for on-site casting and structural crack sensors for UHPCbased structures. Finally, the dispersed CNTs slightly interfered the hydration of the UHPC, but they significantly modified the structure of C-(A)-S-H to be denser, stiffer, and more complex than that of UHPC without CNTs which have been evidenced by observed partial cross-linking in the C-(A)-S-H, reduced d-spacing and the higher fractal dimensions of solid system. Such behaviors were much more significant when EC was applied because the electrical field formed by EC activated ionic polarization and accelerated the chemical reaction among ions in the UHPC matrix, which led to a higher degree of hydration. In conclusion, incorporating appropriate amount of CNTs into UHPC using the proposed dispersion methods can overcome the limitation of UHPC and produce multi-functional UHPC with EMI SE and crack sensing capabilities on-site using EC.이 논문은 뛰어난 기계적 특성 (Mechanical porperties)을 보유했을 뿐만 아니라 전자파 간섭 (Electromagnetic interference, EMI)에 대한 차폐 효과 (Shielding effectiveness, SE), 구조 헬스 모니터링 (Structural health monitoring, SHM) 적용을 위한 균열 자기 감지 (Crack self-sensing) 등 다양한 기능 발휘로 복합 응용이 가능한 초고성능 콘크리트 (Ultra-high performance concrete, UHPC)를 개발하고 현장 시공이 가능토록 하는 것을 목적으로 하였다. 이 연구의 목적을 달성하기 위해 탄소나노튜브 (Carbon Nanotubes, CNTs)를 핵심 재료로 선정하여 UHPC에 혼입하고 분산 방법 (dispersion methods), 기계적 특성, 전자파 차폐, 균열 자기 감지, 전기 경화 (Electrical curing, EC), 그리고 미세구조 변형 (Microstructural modifications) 관점에서 논의 하였다. CNT를 UHPC에 효과적으로 분산시키기 위한 방법 중 하나로 오존 처리 (Ozone treatment)를 적용하고 이에 따른 UHPC의 수화 반응 (Hydration reaction)을 조사하였다. 오존 처리는 산소 및 카르복실기 (Oxygenic and Carboxylic chemical group)를 CNT 표면과 UHPC 입자 주위에 형성하여 효과적으로 CNT를 분산시키고 UHPC의 계면활성작용 (Interfacial interaction)을 개선하였다. 오존 처리는 UHPC 수화 반응에 큰 영향을 미치지는 않았지만 CNT의 효과적인 분산을 통해 다중 핵 형성 반응을 (Multiple nucleation)을 촉진하여 UHPC의 초기 수화 반응을 가속화 할 뿐만 아니라 필러 효과 (Filler effect)로 인해 더욱 더 향상된 압축 강도를 발현하는데 기여하였다. 대용량의 CNT를 UHPC에 균일하게 분산시키기 위하여 초음파 처리 (Sonication) 및 초고성능 감수제 (Super plasticizer)를 활용한 전단 혼합 (Shear mixing) 방법을 제안하였다. 그리고 제안된 분산 방법을 통해 제조된 CNT 분산액을 UHPC와 혼합하고 CNT 혼입 중량에 따른 기계적 특성을 조사하였다. 제안된 분산 방법은 혼입 임계점 (Critical incorporation concentration, CIC) 미만에서 CNT를 효과적으로 분산시킬 수 있었으며 공극 충전 (Pore filling), 가교 효과 (Bridging effect), C-S-H (Calcium-silicatehydrates) 치밀화 (Densification) 등을 통해 압축강도 (Compressive strength) 및 탄성계수 (Elastic modulus)를 향상시켰다. 하지만, 혼입 임계점 이상의 CNT가 혼입된 경우에는 분산된 CNT가 일부 응집 되어 (Agglomeration) UHPC의 수화반응을 방해하고 공극 또는 균열로 작용하여 기계적 특성을 약화시켰다. 분산된 CNT는 침투 임계점 (Percolation threshold) 범위 내에서 UHPC의 전기전도도 (Electrical conductivity) 및 전자파 차폐 효과를 극대화 시켰다. 전자파 차폐 효과를 측정하는 두 가지 표준 시험 (ASTM D4935-18, IEEE-STD-299)을 적용한 결과 ASTM D4935-18은 재료 수준에서 차폐 효과의 대략적인 추세를 평가하는데 사용될 수 있으나 실제 구조 수준에서 정확하게 차폐 효과를 측정하기 위해서는 최소 1200 × 1200 mm2 이상의 충분한 입사면적 (Incident area)을 가지는 실험체로 IEEE-STD-299에 의거 실험해야 함이 입증되었다. UHPC내에 균일하게 분산된 CNT는 UHPC의 전기저항률을 현저히 낮추어 약 19–23 V 범위의 저전압 하에서도 증기 양생과 동등 이상의 효과를 발현하는 전기 경화를 가능하게 하였으며 그 결과 압축강도, 탄성계수, 휨 변형 경화 (Deflection hardening) 등의 기계적 특성이 눈에 띄게 향상되었다. 또한, UHPC/CNT 복합재료는 낮은 전기저항률로 인해 압축 및 휨 응력 하에서 탁월한 균열 자기 감지 능력을 발현하여 구조체의 균열 및 안정성 여부를 판단할 수 근거를 제시하였다. 미세구조 분석 결과, CNT는 UHPC의 중장기 수화 반응을 다소 억제하였지만 C-(A)-S-H의 중간층 (Interlayer) 간격을 줄이고 일부를 연결함으로써 UHPC의 미세구조를 더욱 치밀하게 하고 강성을 강화할 뿐만 아니라 복잡하게 하였다. 이러한 현상은 전기 경화를 적용했을 때 더욱 심화되었는데 이는 전기 경화에 의해 UHPC 매트릭스 내에 형성된 전기장으로 인해 이온 분극 및 화학반응이 가속화되어 수화도가 향상되었기 때문이다. 결론적으로 이 연구에서 제안한 분산 방법을 적용하여 목적에 맞게 적절한 양의 CNT를 UHPC에 혼입하면 UHPC의 한계점을 극복하고 뛰어난 기계적 특성을 발현하는 가운데 전기 경화를 통해 현장 타설이 가능하며 전자파 차폐, 균열 자기 감지 등 다기능 복합 응용이 가능한 UHPC 개발이 가능하다.Chapter 1. Introduction 1 1.1 Background 1 1.1.1 The latest issues in concrete technology 1 1.1.2 Ultra High-Performance Concrete (UHPC) 2 1.1.3 Carbon Nanotubes (CNTs) 4 1.1.4 Compatibility between UHPC and CNTs 6 1.2 Objectives and structure of thesis 9 Chapter 2. Preliminary Study 11 2.1 Literature review 11 2.1.1 Methods to disperse CNTs 11 2.1.2 Cementitious materials incorporated with CNTs for applying EMI shielding 13 2.1.3 Cementitious materials embedded with CNTs for applying SHM techniques 16 2.1.4 Electrical curing (EC) of cementitious materials 18 2.2 Materials used in this study 19 2.3 Basics of microstructural analysis used in this study 21 2.3.1 X-ray diffraction (XRD) 21 2.3.2 Thermogravimetric (TG) analysis 23 2.3.3 Solid-state 29Si nuclear magnetic resonance (NMR) 24 2.3.4 Isothermal calorimetry 25 2.3.5 Mercury intrusion porosimetry (MIP) 27 2.3.6 Small angle X-ray scattering (SAXS) 29 Chapter 3. Ozone Treatment for the Dispersion of CNTs and Hydration Acceleration of UHPC 32 3.1 Introduction 33 3.2 Experimental Details 33 3.2.1 Properties of CNTs 33 3.2.2 Mixture proportions and sample preparation 35 3.2.3 Test methods 38 3.3 Results and Discussion 41 3.3.1 Effect of ozone treatment on dispersion of CNTs 41 3.3.2 Effect of ozone treatment on hydration of UHPC composite 52 3.4 Conclusions 62 Chapter 4. Effect of CNTs on Mechanical Properties of UHPC 64 4.1 Introduction 65 4.2 Experimental Details 65 4.2.1 Properties of CNTs 65 4.2.2 Mixture proportions and sample preparation 65 4.2.3 Test methods 69 4.3 Results and Discussion 70 4.3.1 Degree of dispersion of CNTs 70 4.3.2 Effect of CNTs on Mechanical properties of UHPC 76 4.4 Conclusions 80 Chapter 5. Role of CNTs in the Electromagnetic Shielding Effectiveness of UHPC 83 5.1 Introduction 84 5.2 Experimental Details 84 5.2.1 Mixture proportions and sample preparation 84 5.2.2 Test methods 87 5.3 Results and Discussion 93 5.3.1 Electrical resistivity and conductivity 93 5.3.2 Basics of EMC theory 96 5.3.3 EMI SE results based on two different SE testing method 102 5.3.4 Effects of incident area size on EMI SE 105 5.3.5 Modelling to practically estimate SE of UHPC 115 5.4 Conclusions 126 Chapter 6. Electrically Cured UHPC with CNTs for Field Casting and Crack Self-sensing 128 6.1 Introduction 129 6.2 Experimental Details 129 6.2.1 Properties of CNTs 129 6.2.2 Mixture proportions and sample preparation 132 6.2.3 Test methods 134 6.3 Results and Discussion 137 6.3.1 Flowability 137 6.3.2 Change in temperature and electrical resistivity during curing 137 6.3.3 Morphology of the CNTs in the UHPC 145 6.3.4 Poromechanical properties 147 6.3.5 Compressive strength, elastic modulus, and FCR 149 6.3.6 Flexural strength and FCR 156 6.4 Conclusions 161 Chapter 7. Micro- and Meso-Structural Changes of UHPC by CNTs 164 7.1 Introduction 165 7.2 Experimental Details 165 7.2.1 Mixture proportions and sample preparation 165 7.2.2 Test methods 166 7.3 Results 167 7.3.1 X-ray diffraction 167 7.3.2 Thermogravimetric anlaysis 170 7.3.3 29Si NMR spectroscopy 172 7.3.4 Small angle X-ray scattering 179 7.4 Discussion 190 7.5 Conclusions 193 Chapter 8. Conclusions 195 Reference 201 Appendix 218 초 록 224Docto