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Structural Health Monitoring of Buildings, Bridges and Dams

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A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 27355

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Special Issue Editors

Dr. Husam Hussein

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Guest Editor

1. Department of Civil Engineering, Ohio University, Athens, OH 45701-2979, USA
2. Bridge Engineer at 2LMN, Inc., 2LMN, Lancaster, OH 43130, USA
Interests: uzltra-high-performance concrete; finite element method; structural connection; structural health monitoring; bond strength; composite structures; thermal stresses; load and resistance factor design; structural deterioration and defects; concrete bridges

Dr. Yanping Zhu

E-Mail Website
Guest Editor

Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA
Interests: ultra-high-performance concrete; structural health monitoring; smart materials and structures; topology optimization; digital construction methods; bridge engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Rafea F. Hassan

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Guest Editor

Civil Engineering Department, College of Engineering, University of Babylon, Babylon, Iraq
Interests: ultra-high-performance concrete; finite element method; structural analysis; rehabilitation; torsional behavior; fiber-reinforced concrete; dynamic analysis; concrete bridges; strengthening

Special Issue Information

Dear Colleagues,

The sustainable structures such as buildings, bridges, and dams and their installations is a crucial issue to be maintained against various factors such as deterioration, excessive loads, environment, temperature, etc. Selecting an applicable monitoring system for each structure type is important for determining any critical damage to a as buildings, bridges, or dams to avoid any structural failure. Structural Health Monitoring has developed as an effective technique to monitor the health of the infrastructure. In order to help researchers and engineers to evaluate the integrity and safety of structures, Structural Health Monitoring refers to an ongoing structural performance assessment using different kinds of sensors.

The main aim of this Special Issue " Structural Health Monitoring of Buildings, Bridges and Dams" in Buildings is to provide a platform for the discussion of the major research challenges and achievements on the development of novel Structural Health Monitoring strategies for identifying the location and severity of structural damages by considering any changes in characteristics of the structures.

This Special Issue provides an integrated view of the problems associated with the achievement of Structural Health Monitoring strategies for buildings, bridges, and dams under deterioration, excessive loads, environment, temperature, etc. and the trends in the development of structural damage identification methods for in-service buildings, bridges, and dams.

We warmly invites authors to submit their papers for potential inclusion in this Special Issue on structural health monitoring of buildings, bridges, and dams.

Dr. Husam Hussein
Dr. Yanping Zhu
Dr. Rafea F. Hassan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

structural health monitoring
buildings
bridges
dams
structural deterioration
structural defects
structural failure
structural damage
structural fire
machine learning and deep learning

Published Papers (13 papers)

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Research

22 pages, 1630 KiB

Open AccessArticle

Optimizing Machine Learning Algorithms for Improving Prediction of Bridge Deck Deterioration: A Case Study of Ohio Bridges

by Armin Rashidi Nasab and Hazem Elzarka

Buildings 2023, 13(6), 1517; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13061517 - 12 Jun 2023

Cited by 13 | Viewed by 2241

Abstract

The deterioration of a bridge’s deck endangers its safety and serviceability. Ohio has approximately 45,000 bridges that need to be monitored to ensure their structural integrity. Adequate prediction of the deterioration of bridges at an early stage is critical to preventing failures. The objective of this research was to develop an accurate model for predicting bridge deck conditions in Ohio. A comprehensive literature review has revealed that past researchers have utilized different algorithms and features when developing models for predicting bridge deck deterioration. Since, there is no guarantee that the use of features and algorithms utilized by past researchers would lead to accurate results for Ohio’s bridges, this research proposes a framework for optimizing the use of machine learning (ML) algorithms to more accurately predict bridge deck deterioration. The framework aims to first determine “optimal” features that can be related to deck deterioration conditions, specifically in the case of Ohio’s bridges by using various feature-selection methods. Two feature-selection models used were XGboost and random forest, which have been confirmed by the Boruta algorithm, in order to determine the features most relevant to deck conditions. Different ML algorithms were then used, based on the “optimal” features, to select the most accurate algorithm. Seven machine learning algorithms, including single models such as decision tree (DT), artificial neural networks (ANNs), k-nearest neighbors (k-NNs), logistic regression (LR), and support vector machines (SVRs), as well as ensemble models such as Random Forest (RF) and eXtreme gradient boosting (XGboost), have been implemented to classify deck conditions. To validate the framework, results from the ML algorithms that used the “optimal” features as input were compared to results from the same ML algorithms that used the “most common” features that have been used in previous studies. On a dataset obtained from the Ohio Department of Transportation (ODOT), the results indicated that the ensemble ML algorithms were able to predict deck conditions significantly more accurately than single models when the “optimal” features were utilized. Although the framework was implemented using data obtained from ODOT, it can be successfully utilized by other transportation agencies to more accurately predict the deterioration of bridge components. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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17 pages, 6152 KiB

Open AccessArticle

Experimental Investigation of Compound Effect of Flexural and Torsion on Fiber-Reinforced Concrete Beams

by Nabeel H. Al-Salim, Muna H. Jaber, Rafea F. Hassan, Nisreen S. Mohammed and Husam H. Hussein

Buildings 2023, 13(5), 1347; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13051347 - 21 May 2023

Cited by 2 | Viewed by 1453

Abstract

Fiber-reinforced concrete is widely acknowledged for its ability to resist cracking effectively and limit its propagation. By preventing cracks from spreading, the addition of fiber composites to concrete can enhance its extensibility and tensile strength, not only at the initial point of cracking but also at its maximum capacity. Additionally, the fibers in fiber-reinforced concrete are capable of binding the matrix, even when exposed to significant cracking. However, there is limited information available about the behavior of fiber-reinforced concrete under a bending moment combined with torsion. This study aims to investigate the structural behavior of fiber-reinforced concrete members subjected to a bending moment with a torsion to moment ratio equal to 1. Synthetic and steel fibers of 1.0% content with different lengths (19, 35, and 55 mm for synthetic fiber and 13 mm for straight and hook steel fibers) were mixed with concrete mixtures to examine the effects of fiber lengths and types on the concrete beam performance. Test results indicated that the fiber-reinforced concrete beams showed higher cracking moments than the normal-strength concrete beam. The steel fiber with a hooked configuration reinforced beam showed increased moment capacity and total torsional toughness higher than that of the straight steel fiber-reinforced beam. The synthetic fiber of a 55 mm length reinforced beam exhibited the highest first-crack and ultimate moment values among other tested beams. The test results were compared with past research models for the moment capacity of beams under the compound effect of bending and torsion and we modified these values with another factor that represented the fiber length influence on beam capacity, as suggested in past research. The comparison between the ultimate moment of the test results and the moment predicted from the modified past research model presented a good correlation. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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21 pages, 8678 KiB

Open AccessArticle

Damage Detection in Reinforced Concrete Member Using Local Time-Frequency Transform Applied to Vibration Measurements

by Ning Liu, Thomas Schumacher, Yan Li, Lina Xu and Bo Wang

Buildings 2023, 13(1), 148; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13010148 - 06 Jan 2023

Cited by 5 | Viewed by 1800

Abstract

Signal processing and analysis of structural vibration measurements are key components of structural damage detection (SDD) in structural health monitoring (SHM). The goal of signal processing is to extract subtle changes in the measured signals, which can be used to infer changes in structural parameters and damage. Time-frequency analysis is one of the most popular characterization methods for studying non-stationary vibration signals. In this article, the local time-frequency transform (LTFT) is applied and evaluated to calculate the time-domain signals because of its excellent time-frequency energy distribution properties. The LTFT matches the input data by the Fourier basis in an inverse problem framework and uses the least squares method to solve the time-varying Fourier coefficients. Subsequently, it defines the time-frequency spectrum as the calculated time-varying Fourier coefficients. While the LTFT has been used in the field of geophysics for seismic data processing, its application to structural vibration signals is novel. Both synthetic signals as well as signals collected from a large-scale laboratory test of a reinforced concrete girder were processed with the LTFT and compared with Rényi entropy for quantifying the time-frequency spectrum, the time-frequency resolution abilities of short time Fourier transform (STFT), and S transform (ST). The results show that the LTFT is superior to the traditional time-frequency analysis schemes, in that it is more effective in identifying the energy changes in the time-frequency spectrum before and after structural damage in the form of cracking has occurred. At the same time, it provides high-precision time-frequency resolution and excellent noise suppression abilities. The effectiveness and feasibility of the LTFT applied to the synthetic and experimental signals are verified. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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17 pages, 6762 KiB

Open AccessArticle

Monitoring of Wind Effects on a Super-Tall Building under a Typhoon

by Haoran Pan, Jiurong Wu and Jiyang Fu

Buildings 2023, 13(1), 47; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13010047 - 25 Dec 2022

Cited by 2 | Viewed by 1447

Abstract

Field measurements are critical to further understand the structural behavior of super-tall buildings under strong wind actions. This paper presents field measurements that reflect the wind characteristics and wind effects on Leatop Plaza under Typhoon Vicente. Wind field characteristics, including the turbulence intensity, gust factor, and power spectral density of wind speed in an urban area, were obtained on the basis of a statistical analysis of measured wind data. Subsequently, measured wind-induced accelerations were used to evaluate the dynamic characteristics of the building and the effects of wind on it. On the basis of the first several modes, the modal properties, i.e., the natural frequency and damping ratio, were identified via the fast Bayesian fast Fourier transform method and compared with those identified using the stochastic subspace method. The discrepancy between the identified results and finite element model predictions is presented and discussed. Finally, the variation in the modal parameters with respect to time and the vibration amplitude was analyzed while considering the associated posterior uncertainty. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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18 pages, 16231 KiB

Open AccessArticle

A Novel Structural Damage Identification Method Using a Hybrid Deep Learning Framework

by Yingying He, Zhenghong Huang, Die Liu, Likai Zhang and Yi Liu

Buildings 2022, 12(12), 2130; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12122130 - 04 Dec 2022

Cited by 7 | Viewed by 1618

Abstract

In the past few years, structural health monitoring (SHM) has become an important technology to ensure the safety of structures. Structural damage identification methods based on machine learning techniques have gained wide attention due to the advantages of effectively extracting features from monitoring data. However, the existing machine learning-based methods heavily depend on manually selected feature parameters from raw signals. This will cause the selected feature to obtain the optimal solution for a specific condition but may fail to provide a similar performance in other cases. In addition, the feature selection takes a long time, which can fail to achieve real-time performance in a practical structure. To address these problems, this article proposes a hybrid deep learning framework for structural damage identification that includes three components, namely, ensemble empirical mode decomposition (EEMD), Pearson correlation coefficient (PCC), and a convolutional neural network (CNN). The proposed EEMD-PCC-CNN method is capable of automatically extracting features from raw signals to satisfy any damage identification objective. To evaluate the performance of the proposed EEMD-PCC-CNN method, a three-story building structure is investigated. The acceleration signal of the three-story building structure is first analyzed by EEMD. After obtaining the time-frequency information, PCC is utilized to select optimal time-frequency information as the input of the CNN for damage identification. Compared with other classical methods (SVM, KNN, RF, etc.), the experimental results show that the newly proposed EEMD-PCC-CNN method has significant performance advantages in damage identification. In addition, the accuracy of the proposed damage identification method is improved by more than 4% after utilizing EEMD in comparison with CNN alone. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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19 pages, 5385 KiB

Open AccessArticle

Environment-Induced Performance of End Concrete Diaphragm in Skewed Semi-Integral Bridges

by Husam H. Hussein, Issam Khoury and Joshua S. Lucas

Buildings 2022, 12(11), 1985; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12111985 - 16 Nov 2022

Viewed by 1501

Abstract

Past research has shown that as skewed bridges change temperature, additional lateral movement or forces will occur along with the elongation of the bridge. Even though past research has documented this behavior, lateral movements of semi-integral bridge superstructure associated with temperature effects on bridge skewness have not been well predicted. In this study, the seasonal movements of a 24-year-old semi-integral bridge caused by temperature effects with skewed abutment have been investigated by conducting a series of field measurements on bridges subjected to various environmental climates. The measured data showed that as the bridge heated up, the superstructure tended to move toward the acute corner of the bridge, and the bridge would contract towards its obtuse corner with a negative temperature change. During warm weather, the cracks on the end diaphragm tended to open with a positive temperature change and close with a negative temperature change, which was much more predictable than the cold weather behavior. This behavior confirms that even though the bridge moves linearly with temperature, the end diaphragm response to the temperature depends on the season. Movement of the bridge superstructure from temperature change has caused cracks in the end diaphragm, which are now propagating to the deck. These cracks could damage the bridge enough that it would require repair work in the future. The evidence in this study will help provide a complete picture of seasonal jointless bridge behavior so future semi-integral bridges can be made safer and more efficient. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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18 pages, 3608 KiB

Open AccessArticle

Research on a U-Net Bridge Crack Identification and Feature-Calculation Methods Based on a CBAM Attention Mechanism

by Huifeng Su, Xiang Wang, Tao Han, Ziyi Wang, Zhongxiao Zhao and Pengfei Zhang

Buildings 2022, 12(10), 1561; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12101561 - 28 Sep 2022

Cited by 16 | Viewed by 2553

Abstract

Crack detection on bridges is an important part of assessing whether a bridge is safe for service. The methods using manual inspection and bridge-inspection vehicles have disadvantages, such as low efficiency and affecting road traffic. We have conducted an in-depth study of bridge-crack detection methods and have proposed a bridge crack identification algorithm for Unet, called the CBAM-Unet algorithm. CBAM (Convolutional Block Attention Module) is a lightweight convolutional attention module that combines a channel attention module (CAM) and a spatial attention module (SAM), which use an attention mechanism on a channel and spatially, respectively. CBAM takes into account the characteristics of bridge cracks. When the attention mechanism is used, the ability to express shallow feature information is enhanced, making the identified cracks more complete and accurate. Experimental results show that the algorithm can achieve an accuracy of 92.66% for crack identification. We used Gaussian fuzzy, Otsu and medial skeletonization algorithms to realise the post-processing of an image and obtain a medial skeleton map. A crack feature measurement algorithm based on the skeletonised image is proposed, which completes the measurement of the maximum width and length of the crack with errors of 1–6% and 1–8%, respectively, meeting the detection standard. The bridge crack feature extraction algorithm we present, CBAM-Unet, can effectively complete the crack-identification task, and the obtained image segmentation accuracy and parameter calculation meet the standards and requirements. This method greatly improves detection efficiency and accuracy, reduces detection costs and improves detection efficiency. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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16 pages, 5620 KiB

Open AccessArticle

Damage Detection of Steel Truss Bridges Based on Gaussian Bayesian Networks

by Xiaotong Sun, Yu Xin, Zuocai Wang, Minggui Yuan and Huan Chen

Buildings 2022, 12(9), 1463; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12091463 - 15 Sep 2022

Cited by 3 | Viewed by 1730

Abstract

This paper proposes the use of Gaussian Bayesian networks (GBNs) for damage detection of steel truss bridges by using the strain monitoring data. Based on the proposed damage detection procedure, a three-layer GBN model is first constructed based on the load factors, structural deflections, and the stress measurements of steel truss bridges. More specifically, the load factors of the structures are defined as the first-layer network nodes, structural deflections are considered as the second-layer network nodes, and the third-layer nodes of the GBN model are built based on the stress data of the truss elements. To achieve the training for the constructed GBN model, the finite element analysis of the bridge structures under the different load factors is performed. Then, the training of the network is performing by using the maximum likelihood estimation approach, and the optimized network parameters are obtained. Based on the trained network model, the measured load factors and the corresponding stress monitoring data of a limited number of truss elements are considered as input, and the stress measurements of all truss elements of bridges can be accurately estimated by searching the optimized topological information among network nodes. For a steel truss bridge, when the truss elements are damaged, the stress states of the damaged elements will be changed. Therefore, a damage index is further constructed for damage detection of steel truss bridges based on the changed stress states of those damaged elements. To verify the feasible and effective use of the proposed damage detection approach, an 80 m steel truss bridge with various damage cases was conducted as numerical simulations, and the investigation results show that the trained GBN can be accurately used for stress prediction of steel truss bridges, and the proposed damage index with the estimated stress data can be further applied for structural damage localization and quantification with a better accuracy. Furthermore, the results also suggest that the proposed damage detection procedure is accurate and reliable for steel truss bridges under vehicle loads. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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22 pages, 9202 KiB

Open AccessArticle

Synchro-Squeezed Adaptive Wavelet Transform-Based Optimized Multiple Analytical Mode Decomposition: Parameter Identification of Cable-Stayed Bridge under Earthquake Input

by Hongya Qu, An Chang, Tiantian Li and Zhongguo Guan

Buildings 2022, 12(8), 1285; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12081285 - 22 Aug 2022

Cited by 2 | Viewed by 1672

Abstract

Deriving critical parametric information from recorded signals for system identification is critical in structural health monitoring and damage detection, while the time-varying nature of most signals often requires significant processing efforts due to structural nonlinearity. In this study, synchro-squeezed adaptive wavelet transform-based optimized multiple analytical mode decomposition (SSAWT-oMAMD) is proposed. The SSAWT algorithm acts as the preprocessing algorithm for clear signal component separation, high temporal and frequency resolution, and accurate time–frequency representation. Optimized MAMD is then utilized for signal denoising, decomposition, and identification, with the help of AWT for bisecting frequency determination. The SSAWT-oMAMD is first verified by the analytical model of two Duffing systems, where clear separation of the two signals is presented and identification of complex time-varying stiffness is achieved with errors less than 2.9%. The algorithm is then applied to system identification of a cable-stayed bridge model subjected to earthquake loading. Based on both numerical and experimental results, the proposed method is effective in identifying the structural state and viscous damping coefficient. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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18 pages, 6664 KiB

Open AccessArticle

Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images

by Shuai Teng, Zongchao Liu and Xiaoda Li

Buildings 2022, 12(8), 1225; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12081225 - 12 Aug 2022

Cited by 23 | Viewed by 2028

Abstract

Automatic bridge surface defect detection is of wide concern; it can save human resources and improve work efficiency. The object detection algorithm, especially the You Only Look Once (YOLO) series of networks, has important potential in real-time object detection because of its fast detection speed, and it provides an efficient and automatic detection method for bridge surface defect detection. Hence, this paper employs an improved YOLOv3 network for detecting bridge surface defects (cracks and exposed rebar) and compares the effects of the advanced YOLOv2, YOLOv3 and faster region-based convolutional neural network (Faster RCNN) in bridge surface defect detection, and then two approaches (transfer learning and data augmentation) are used to improve the YOLOv3. The results confirm that, by combining high- and low-resolution feature images, the YOLOv3 improves the detection effect of the YOLOv2 (using single-resolution feature images); the average precision (AP) value of the improved YOLOv3 (0.9–0.91) is 6–10% higher than that of the YOLOv2 (0.83–0.86). Then, the anti-noise abilities of the YOLOv2 and YOLOv3 are studied by introducing white Gaussian noise, and the YOLOv3 is better than the YOLOv2. Simultaneously, the YOLO series of detectors perform better in detection speed; the detection speed of the improved YOLOv3 (FPS (frames per second) = 23.8) is 103 times that of the Faster RCNN (FPS = 0.23) with comparable mAP values (improved YOLOv3 = 0.91; Faster RCNN = 0.9). It is demonstrated that, in consideration of detection precision and speed, the proposed improved YOLOv3 is a decent detector for fast and real-time bridge defect detection. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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22 pages, 8653 KiB

Open AccessArticle

Comparative Analysis of Dynamic Response of Damaged Wharf Frame Structure under the Combined Action of Ship Collision Load and Other Static Loads

by Mingjie Zhao, Guoyin Wu and Kui Wang

Buildings 2022, 12(8), 1131; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12081131 - 30 Jul 2022

Cited by 4 | Viewed by 1838

Abstract

In the long-term service, the wharf structure can be damaged by ship impact, wave load, and even earthquake, which will affect the safe production and smooth operation of the port. Based on the theory of structural dynamic response analysis and wavelet packet analysis principle, this paper established the damage identification index of the wharf frame structure. Combining with the finite element method and the dynamic response theory of the wharf frame structure, it set up a finite element analysis model of the dynamic response of the wharf frame structure under the action of multiple loads, with the impact load of the ship as the dynamic load under the non-damaged state and the different damaged states. In addition, the characteristic response point location was drawn up. Furthermore, the transient dynamic analysis and damage index analysis of the frame structure in the non-damaged and damaged state were conducted respectively. In addition, the model test and numerical simulation results were combined to compare and analyze the identification of damage indicators, so as to verify the identification effect of the established damage identification indicators on the structural damage, which lays a foundation for the next step of structural damage identification. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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21 pages, 3467 KiB

Open AccessArticle

Structural Health Monitoring of a Brazilian Concrete Bridge for Estimating Specific Dynamic Responses

by Enrico Zacchei, Pedro H. C. Lyra, Gabriel E. Lage, Epaminondas Antonine, Airton B. Soares, Jr., Natalia C. Caruso and Cassia S. de Assis

Buildings 2022, 12(6), 785; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12060785 - 08 Jun 2022

Cited by 2 | Viewed by 1972

Abstract

A 3D coupled model to simulate vehicle–bridge interactions (VBI) to estimate its structural responses and impact factors (IMs) was developed in this study. By structural health monitoring (SHM) of a real concrete bridge, several data were collected to calibrate the bridge model by the finite element method (FEM). These models provide the bridge response in terms of vertical displacements and accelerations. VBI models provide reliable outputs without significantly altering the dynamic properties of the bridge. Modified recent analytical equations, which account for the effects of the asymmetric two-axle vehicles, were developed numerically. These equations, plus some proposed solutions, also quantified the vehicle response in terms of accelerations to estimate a more conservative driving comfort. The goal consisted in fitting the SHM with numerical and analytical models to find a more appropriate response for safety purposes and maintenance. From the codes and the literature, it was shown that a unique IM factor was not found. Moreover, most approaches underestimate the phenomena; in fact, results show that a monitored IM factor is 2.5 greater than IM from codes. Proposed equations for vehicle accelerations provided more conservative values up to about three times the standard comfort value. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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21 pages, 50158 KiB

Open AccessFeature PaperArticle

Experimental and Analytical Investigation of Bond Behavior of Deformed Steel Bar and Ultra-High Performance Concrete

by Rui Liang, Yuan Huang and Zhenming Xu

Buildings 2022, 12(4), 460; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12040460 - 08 Apr 2022

Cited by 19 | Viewed by 3098

Abstract

Ultra-high performance concrete (UHPC) has been demonstrated to be a realistic alternative to less maintenance and significantly longer service life due to its better mechanical properties and low permeability. The bond performance of the deformed steel bar embedded in UHPC is critically important for the safety of the UHPC structures. This paper conducted an experimental investigation on the bond behavior of deformed steel bars and UHPC. The impacts of loading method, UHPC strength, steel fiber type and content, rebar diameter, and cover thickness were studied. The testing results revealed that the specimens failed in three modes: pull-out, splitting + pull-out, and cone failure. The main factors affecting the bond strength are UHPC compressive strength, cover thickness, and fiber characteristics. The peak slip of rebar-UHPC increases with cover thickness and rebar diameter. Finally, an analytical model of the bond stress-slip relationship between the UHPC and deformed steel bar is obtained, which is in suitable agreement with the test results. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Buildings, Bridges and Dams)

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