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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 31347

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Prof. Dr. Bijan Samali

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Guest Editor

Centre for Infrastructure Engineering, School of Engineering, Design and Built Environment, Western Sydney University, NSW, Australia
Interests: structural dynamics; earthquake engineering; wind engineering; smart materials for structural control applications; damage detection and health monitoring of bridges
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Dr. Maria Rashidi

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Guest Editor

Senior Lecturer, Centre for Infrastructure Engineering, Western Sydney University, Kingswood, NSW 2747, Australia
Interests: bridge engineering and asset management; digital twin development; unmanned aerial vehicle (UAV) based photogrammetry; terrestrial laser scanning (TLS); structural health monitoring (SHM), sustainability, and life cycle management.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce the publication of a Special issue of the Journal of Materials with the theme of “Emerging Trends in Structural Health Monitoring”, which is rapidly evolving and is embracing many new technologies in meeting the critical needs of our global ageing infrastructure. This is now a multidisciplinary area of research and development, and you are warmly invited to consider submitting your cutting-edge research for consideration. Some, but not all areas of focus include:

Condition assessment of civil, mechanical, aerospace, and offshore structures, as well as connections of structural elements;
Diagnostics of cultural heritage monuments;
Testing of structures made of novel materials;
Structural health monitoring (SHM) systems;
Integration of non-destructive testing methods (e.g., guided waves, ground penetrating radar, acoustic emission, thermography);
Advanced signal processing for NDT;
Damage detection and damage imaging;
Modelling and numerical analyses for supporting SHM systems;
Utilisation of unmanned arial vehicles (UAVs) for SHM;
Artificial Intelligence (AI) for SHM;
Digital twin development of structures using terrestrial laser scanner (TLS) and UAV photogrammetry.

Prof. Dr. Bijan Samali
Dr. Maria Rashidi
Guest Editors

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Keywords

condition assessment
structural health monitoring systems
damage detection
non-destructive testing
utilisation of UAVs
advanced signal processing
artificial intelligence and machine learning
digital twinning
terrestrial laser scanner (TLS)

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Published Papers (11 papers)

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Research

21 pages, 4403 KiB

Open AccessArticle

Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam

by Zhaoqiu Lyu, Yang Yu, Bijan Samali, Maria Rashidi, Masoud Mohammadi, Thuc N. Nguyen and Andy Nguyen

Materials 2022, 15(4), 1477; https://doi.org/10.3390/ma15041477 - 16 Feb 2022

Cited by 61 | Viewed by 5009

Abstract

Due to the limitation of sample size in predicting the torsional strength of Reinforced Concrete (RC) beams, this paper aims to discuss the feasibility of employing a novel machine learning approach with K-fold cross-validation in a small sample range, which combines the advantages of a Genetic Algorithm (GA) and a Neural Network (NN) to predict the torsional strength of RC beams. This research study not only utilizes the application of a Back Propagation (BP) neural network and the Gene Algorithm-Back Propagation (GA-BP) neural network in the prediction of the torsional strength of the RC beam, but it also investigates neural network parameter optimization, including connection weights and thresholds, using K-fold cross-validation. The root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and correlation coefficient (R²) are among the evaluation metrics used to assess the performance of the trained model. To elaborate on the superiority of the proposed network models in predicting the torsional strength of RC beams, a parametric study is conducted by comparing the proposed model to three commonly used empirical formulae from existing design codes. The comparative findings of this research study demonstrate that the performance of the BP neural network is highly similar to that of design codes; however, its accuracy is inadequate. After improving the weights and thresholds by k-fold cross-validation and GA, the prediction of the BP neural network shows higher consistency with the actual measured values. The outcome of this study can be used as a theoretical reference for the optimal design of RC beams in practical applications. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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18 pages, 3399 KiB

Open AccessArticle

Lamb Wave Based Structural Damage Detection Using Stationarity Tests

by Phong B. Dao and Wieslaw J. Staszewski

Materials 2021, 14(22), 6823; https://doi.org/10.3390/ma14226823 - 12 Nov 2021

Cited by 8 | Viewed by 1806

Abstract

Lamb waves have been widely used for structural damage detection. However, practical applications of this technique are still limited. One of the main reasons is due to the complexity of Lamb wave propagation modes. Therefore, instead of directly analysing and interpreting Lamb wave propagation modes for information about health conditions of the structure, this study has proposed another approach that is based on statistical analyses of the stationarity of Lamb waves. The method is validated by using Lamb wave data from intact and damaged aluminium plates exposed to temperature variations. Four popular unit root testing methods, including Augmented Dickey–Fuller (ADF) test, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, Phillips–Perron (PP) test, and Leybourne–McCabe (LM) test, have been investigated and compared in order to understand and make statistical inference about the stationarity of Lamb wave data before and after hole damages are introduced to the aluminium plate. The separation between t-statistic features, obtained from the unit root tests on Lamb wave data, is used for damage detection. The results show that both ADF test and KPSS test can detect damage, while both PP and LM tests were not significant for identifying damage. Moreover, the ADF test was more stable with respect to temperature changes than the KPSS test. However, the KPSS test can detect damage better than the ADF test. Moreover, both KPSS and ADF tests can consistently detect damages in conditions where temperatures vary below 60 °C. However, their t-statistics fluctuate more (or less homogeneous) for temperatures higher than 65 °C. This suggests that both ADF and KPSS tests should be used together for Lamb wave based structural damage detection. The proposed stationarity-based approach is motivated by its simplicity and efficiency. Since the method is based on the concept of stationarity of a time series, it can find applications not only in Lamb wave based SHM but also in condition monitoring and fault diagnosis of industrial systems. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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20 pages, 5420 KiB

Open AccessArticle

Utilizing Artificial Intelligence to Predict the Superplasticizer Demand of Self-Consolidating Concrete Incorporating Pumice, Slag, and Fly Ash Powders

by Jing Liu, Masoud Mohammadi, Yubao Zhan, Pengqiang Zheng, Maria Rashidi and Peyman Mehrabi

Materials 2021, 14(22), 6792; https://doi.org/10.3390/ma14226792 - 11 Nov 2021

Cited by 54 | Viewed by 2242

Abstract

Self-consolidating concrete (SCC) is a well-known type of concrete, which has been employed in different structural applications due to providing desirable properties. Different studies have been performed to obtain a sustainable mix design and enhance the fresh properties of SCC. In this study, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design. For this purpose, a comprehensive database consisting of verified test results of SCC incorporating cement replacement powders including pumice, slag, and fly ash (FA) has been employed. In this regard, at first, fresh properties tests including the J-ring, V-funnel, U-box, and different time interval slump values were considered to collect the datasets. At the second stage, five models of ANFIS were adjusted and the most precise method for predicting the SP demand was identified. The correlation coefficient (R²), Pearson’s correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and Wilmot’s index of agreement (WI) were used as the measures of precision. Later, the most effective parameters on the prediction of SP demand were evaluated by the developed ANFIS. Based on the analytical results, the employed algorithm was successfully able to predict the SP demand of SCC with high accuracy. Finally, it was deduced that the V-funnel test is the most reliable method for estimating the SP demand value and a significant parameter for SCC mix design as it led to the lowest training root mean square error (RMSE) compared to other non-destructive testing methods. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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15 pages, 6056 KiB

Open AccessArticle

Fresh, Mechanical, and Durability Properties of Self-Compacting Mortar Incorporating Alumina Nanoparticles and Rice Husk Ash

by Bahareh Mehdizadeh, Soheil Jahandari, Kirk Vessalas, Hania Miraki, Haleh Rasekh and Bijan Samali

Materials 2021, 14(22), 6778; https://doi.org/10.3390/ma14226778 - 10 Nov 2021

Cited by 26 | Viewed by 2321

Abstract

This paper presents a comprehensive evaluation on self-compacting (SC) mortars incorporating 0, 1, 3, and 5% alumina nanoparticles (NA) as well as 0% and 30% rice husk ash (RHA) used as Portland cement replacement. To evaluate the workability, mechanical, and durability performance of SC mortars incorporating NA and RHA, the fresh properties (slump flow diameter and V-funnel flow time), hardened properties (compressive strength, flexural strength, and ultrasonic pulse velocity), and durability properties (water absorption, rapid chloride permeability, and electrical resistivity) were determined. The results indicated that the addition of NA and RHA has negligible effect on the workability and water absorption rate of the SC mortars. However, significant compressive and flexural strength development was observed in the SC mortars treated with NA or the combination of NA and RHA. The introduction of RHA and NA also reduced the rapid chloride permeability and enhanced the electrical resistivity of the SC mortars significantly. It is concluded that the coexistence of 30% RHA and 3% NA as cement replacement in SC mortars can provide the best mechanical and durability performance. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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24 pages, 10029 KiB

Open AccessArticle

Analysis of Fire Resistance of Square-Cased Square Steel Tube Reinforced Concrete (ST-RC) Columns

by Gaoxiong Wang, Yanhong Bao, Li Yang and Yang Yu

Materials 2021, 14(19), 5541; https://doi.org/10.3390/ma14195541 - 24 Sep 2021

Cited by 3 | Viewed by 2120

Abstract

Based on the finite element (FE) analysis software Abaqus, an FE model of square-cased square steel tube reinforced concrete (ST-RC) columns under the hybridized action of high-temperature and load is established. The accuracy of the FE model is verified using experimental data from existing studies. This model is used to analyze the temperature change, internal force distribution, and failure characteristics of the square-cased square ST-RC columns under the action of fire, as well as the factors affecting the fire resistance limit of the column. The results of FE analysis show that under the action of fire, the maximum internal temperature of the square-cased square ST-RC columns occurs in the corner of the section. Moreover, the stress and strain reach their maximum values at the concrete corner outside the tube. During the heating process, an internal force redistribution occurs in the square-cased square ST-RC column. At the same time, the proportion of the axial force and the bending moment of the reinforced concrete outside the pipe decreases gradually, while the proportion of the internal force of the core concrete-filled steel tube (CFST) increases gradually. In essence, it is a process of load transfer from the high-temperature to the low-temperature zone. In addition, the section size, load ratio, slenderness ratio, cross-sectional core area ratio, steel content, and external concrete strength are the main parameters affecting the fire resistance limit of the square-cased square ST-RC columns. Among them, the cross-sectional core area ratio, section size, steel ratio, and external concrete strength are positively correlated with the fire resistance limit of the composite column. On the contrary, with the increase in the load ratio and the slenderness ratio, the fire resistance limit of the square-cased square ST-RC columns decreases. On this basis, a simplified formula to calculate the fire resistance limit of square-cased square ST-RC columns is proposed. The research results can be used as a theoretical reference for the fire protection design of this kind of structure in practical engineering. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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10 pages, 1220 KiB

Open AccessArticle

Progressive Collapse Safety Evaluation of Truss Structures Considering Material Plasticity

by Sheng-En Fang, Chen Wu, Xiao-Hua Zhang, Li-Sen Zhang, Zhi-Bin Wang and Qing-Yi Zeng

Materials 2021, 14(18), 5135; https://doi.org/10.3390/ma14185135 - 7 Sep 2021

Cited by 5 | Viewed by 1849

Abstract

Theoretical or numerical progressive collapse analysis is necessary for important civil structures in case of unforeseen accidents. However, currently, most analytical research is carried out under the assumption of material elasticity for problem simplification, leading to the deviation of analysis results from actual situations. On this account, a progressive collapse analysis procedure for truss structures is proposed, based on the assumption of elastoplastic materials. A plastic importance coefficient was defined to express the importance of truss members in the entire system. The plastic deformations of members were involved in the construction of local and global stiffness matrices. The conceptual removal of a member was adopted, and the impact of the member loss on the truss system was quantified by bearing capacity coefficients, which were subsequently used to calculate the plastic importance coefficients. The member failure occurred when its bearing capacity arrived at the ultimate value, instead of the elastic limit. The extra bearing capacity was embodied by additional virtual loads. The progressive collapse analysis was performed by iterations until the truss became a geometrically unstable system. After that, the critical progressive collapse path inside the truss system was found according to the failure sequence of the members. Lastly, the proposed method was verified against both analytical and experimental truss structures. The critical progressive collapse path of the experimental truss was found by the failure sequence of damaged members. The experimental observation agreed well with the corresponding analytical scenario, proving the method feasibility. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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21 pages, 39248 KiB

Open AccessEditor’s ChoiceArticle

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

by Yuping Feng, Masoud Mohammadi, Lifeng Wang, Maria Rashidi and Peyman Mehrabi

Materials 2021, 14(17), 4885; https://doi.org/10.3390/ma14174885 - 27 Aug 2021

Cited by 59 | Viewed by 2454

Abstract

This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R²). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R² = 0.9871 in the testing phase. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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22 pages, 95821 KiB

Open AccessArticle

Diagnostics of Large Non-Conductive Anti-Corrosion Coatings on Steel Structures by Means of Electrochemical Impedance Spectroscopy

by Tomasz Jaśniok, Mariusz Jaśniok and Artur Skórkowski

Materials 2021, 14(14), 3959; https://doi.org/10.3390/ma14143959 - 15 Jul 2021

Cited by 7 | Viewed by 2432

Abstract

This paper proposes a testing methodology for barrier properties of large non-conductive anti-corrosion coatings on steel structures. Electrochemical impedance spectroscopy (EIS) was adapted to in situ testing of steel structures by using a prototypical flexible measuring probe and a gel electrolyte that filled the probe, to take measurements on any surface regardless of its position. The first stage of the testing methodology was to perform time-consuming impedance measurements and quick electromagnetic measurements of coating thickness at selected test points. The results were used to determine correlation relationships between the logarithm of the impedance modulus for the coating at a measuring frequency of 0.1 Hz measured with the EIS method and the average thickness of the coating measured with an electromagnetic thickness gauge. Quick electromagnetic measurements were performed in the second stage to specify thickness of the other surface of the steel structure coating. The barrier properties of this coating were identified on the basis of the determined correlation. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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14 pages, 5262 KiB

Open AccessArticle

Numerical Analysis of Axial Cyclic Behavior of FRP Retrofitted CHS Joints

by Mohammad Alembagheri, Maria Rashidi, Amin Yazdi and Bijan Samali

Materials 2021, 14(3), 648; https://doi.org/10.3390/ma14030648 - 31 Jan 2021

Cited by 5 | Viewed by 2229

Abstract

This paper aims to numerically investigate the cyclic behavior of retrofitted and non-retrofitted circular hollow section (CHS) T-joints under axial loading. Different joints with varying ratios of brace to chord radius are studied. The effects of welding process on buckling instability of the joints in compression and the plastic failure in tension are considered. The finite element method is employed for numerical analysis, and the SAC protocol is considered as cyclic loading scheme. The CHS joints are retrofitted with different numbers of Fiber Reinforced Polymer (FRP) layers with varying orientation. The results show that the welding process significantly increases the plastic failure potential. The chord ovalization is the dominant common buckling mode under the compression load. However, it is possible to increase the energy dissipation of the joints by utilizing FRP composite through changing the buckling mode to the brace overall buckling. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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19 pages, 12502 KiB

Open AccessArticle

Estimating Compressive Strength of Concrete Containing Untreated Coal Waste Aggregates Using Ultrasonic Pulse Velocity

by Mahmood Karimaei, Farshad Dabbaghi, Mehdi Dehestani and Maria Rashidi

Materials 2021, 14(3), 647; https://doi.org/10.3390/ma14030647 - 31 Jan 2021

Cited by 29 | Viewed by 3374

Abstract

In recent years, the overuse and exploitation of coal resources as fuel in industry has caused many environmental problems as well as changes in the ecosystem. One way to address this issue is to recycle these materials as an alternative to aggregates in concrete. Recently, non-destructive tests have also been considered by the researchers in this field. As there is limited work on the evaluation of the compressive strength of concrete containing coal waste using non-destructive tests, the current study aims to estimate the compressive strength of concrete containing untreated coal waste aggregates using the ultrasonic pulse velocity (UPV) technique as a non-destructive testing approach. For this purpose, various concrete parameters such as the compressive strength and UPV were investigated at different ages of concrete with different volume replacements of coarse and fine aggregates with coal waste. The test results indicate that 5% volume replacement of natural aggregates with untreated coal waste improves the average compressive strength and UPV of the concrete mixes by 6 and 1.2%, respectively. However, these parameters are significantly reduced by increasing the coal waste replacement level up to 25%. Furthermore, a general exponential relationship was established between the compressive strength and the UPV associated with the entire tested concrete specimens with different volume replacement levels of coal waste at different ages. The proposed relationship demonstrates a good correlation with the experimental results. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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18 pages, 9559 KiB

Open AccessArticle

Operational Modal Analysis, Testing and Modelling of Prefabricated Steel Modules with Different LSF Composite Walls

by Maria Rashidi, Pejman Sharafi, Mohammad Alembagheri, Ali Bigdeli and Bijan Samali

Materials 2020, 13(24), 5816; https://doi.org/10.3390/ma13245816 - 20 Dec 2020

Cited by 14 | Viewed by 3405

Abstract

The modal properties of modular structures, such as their natural frequencies, damping ratios and mode shapes, are different than those of conventional structures, mainly due to different structural systems being used for assembling prefabricated modular units onsite. To study the dynamic characteristics of modular systems and define a dynamic model, both the modal properties of the individual units and their connections need to be considered. This study is focused on the former aspect. A full-scale prefabricated volumetric steel module was experimentally tested using operational modal analysis technique under pure ambient vibrations and randomly generated artificial hammer impacts. It was tested in different situations: [a] bare (frame only) condition, and [b] infilled condition with different configurations of gypsum and cement-boards light-steel framed composite walls. The coupled module-wall system was instrumented with sensitive accelerometers, and its pure and free vibration responses were synchronously recorded through a data acquisition system. The main dynamic characteristics of the module were extracted using output-only algorithms, and the effects of the presence of infill wall panels and their material are discussed. Then, the module’s numerical micromodel for bare and infilled states is generated and calibrated against experimental results. Finally, an equivalent linear strut macro-model is proposed based on the calibrated data. The contribution of this study is assessing the effects of different infill wall materials on the dynamic characteristics of modular steel units, and proposing simple models for macro-analysis of infilled module assemblies. Full article

(This article belongs to the Special Issue Emerging Trends in Structural Health Monitoring)

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