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The high corrosion resistance, recyclability, ductility, and durability of stainless steels make them appealing for use in the construction industry. However, when used as structural channel sections, they are subject to localised failure in the web. This study aims to evaluate the structural behaviour of cold-formed austenitic stainless steel unlipped channels under localised interior loading. The results of conducting a novel testing programme with 21 new tests on such channels with different cross-section sizes and thicknesses are presented. Then a non-linear quasi-static Finite Element (FE) model is developed. The FE model is validated against experimental test results and found to demonstrate a good agreement in terms of bearing capacity and failure modes. It is found that no cold-formed stainless steel standard provides web bearing capacity equations for the localised interior loading (IL) that apply when simulating a floor joist on a solid foundation. The web bearing capacities are compared with capacities determined from the literature (including DSM). It is found that for the case of unlipped channels (unstiffened flanges), the current equations are unreliable and unconservative by up to 36%. The experimental investigation shows that, the American specification (SEI/ASCE 8:2002) is unconservative as much as 41%. Using both experimental and numerical results from this study, a new design equation is proposed for austenitic stainless steel unlipped channels under localised interior loading.

Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms.

In the current modern era of information and technology, the concept of Building Information Modeling (BIM), has made revolutionary changes in different aspects of engineering design, construction, monitoring, and management of infrastructure assets, especially bridges. In the field of bridge engineering, Bridge Information Modeling (BrIM), as a specific form of BIM, includes digital twinning of bridge assets associated with geometrical information and non-geometrical inspection data. BrIM has demonstrated tremendous potential in substituting traditional paper-based documentation and hand-written reports with digital bridge documentation/transformation, allowing professionals and managers to execute bridge management more efficiently and effectively. However, concerns remain about the quality of the acquired data in BrIM development, as well as lack of research on utilizing these information for remedial actions/decisions in a reliable Bridge Management System (BMS), which are mainly reliant on the knowledge and experience of the involved inspectors, or asset managers, and are susceptible to a certain degree of subjectivity. To address these concerns, this research paper presents a comprehensive methodology as an advanced asset management system that employs BrIM data to improve and facilitate the BMS. This innovative BMS is comprised of a precise Terrestrial Laser Scan (TLS)-derived BrIM as a qualitative digital replica of the existing bridge, incorporating geometrical and non-geometrical information of the bridge elements, and equipped with a requirement-driven framework in a redeveloped condition assessment model for priority ranking of bridge elements based on their health condition. In another step ahead, the proposed BMS integrates a Decision Support System (DSS) to score the feasible remedial strategies and provide more objective decisions for optimal budget allocation and remedial planning. This methodology was further implemented via a developed BrIM-orient

The future development of smart materials in the areas, such as self-tuning vibrating energy harvesting devices, self-sustainable networks, and earthquake protection devices, is of significant importance. With the great potential to develop smart structures, smart material includes shape memory alloy, piezoelectric, magnetorheological (MR) and electrorheological (ER) materials, which possess stimulus responses. Accordingly, smart materials can be regarded as promising candidates for adaptive device development for structural vibration mitigation application. In this chapter, the smart material classification and potential applications are reviewed first. Then, a case study is conducted to comprehensively illustrate application of MR material-based semi-active control device in structural seismic protection. Finally, the challenges and future work on this topic are briefly analysed and discussed.

A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties.

Damage of shear connectors in steel-concrete composite (SCC) beams affects the composite action and appears as abnormalities in the shear slip between the composite components. The shear slip at the composite interface causes nonlinearity in the global composite beam response which is an issue beyond the inherent complexity of the composite system. This paper presents a novel approach for damage detection of SCCs by variational mode decomposition (VMD) of shear slip data. Numerical and experimental studies were conducted on steel-concrete composite beams to generate noise-contaminated shear slip data from undamaged and damaged states of the structure. The VMD algorithm is employed to decompose shear slip signals into intrinsic mode functions (IMFs) and then, the center frequency of each mode is captured. The higher center frequency realized in the second mode is taken as a damage sensitive feature. Because IMFs curves are extracted from the signal splines interpolated between the average of the peaks and troughs, the change of energy in the center frequencies of the undamaged and damaged states can be defined as a damage index. Welch power spectral densities of the IMFs are calculated to further investigate changes in the center frequencies of IMFs obtained using the VMD algorithm. The empirical mode decomposition (EMD) technique is also utilized to decompose shear slip signals into IMFs for comparison purposes. The results show that the EMD is not able to detect abnormalities in the shear slip signals affected by damage because of losing information through several mode decompositions and a phenomenon termed mode mixing. However, when the VMD was set to decompose the signal into two IMFs, it was found that it is more efficient in maintaining the frequency content of the shear slip signal. According to the results, the proposed method has been proved successful in detecting damage of composite beams and can be employed as a reliable and robust technique.

AbstractCold‐formed steel sections are used to form various components of low‐and medium‐rise buildings. Such sections are commonly constructed with sheathing board, providing thermal and acoustic separation between interior partitions. In contemporary projects, these sheathing boards have been used to provide bracing and increasing lateral stiffness. The strength and stiffness of sheathing‐braced structural assemblies is governed by connections between framing elements and sheathing components. This paper describes an experimental investigation of stability and strength of sheathed cold‐formed steel lipped sigma wall studs under compression loading. A total of 35 experimental tests conducted covering various parameters including, sheathing plasterboard thickness, stud and track thickness, plasterboard configuration and density. Sheathing density and thickness are found to be significant in impacting the compression strength of the sheathed cold‐formed steel lipped sigma studs. A clear pattern was noted between plasterboard material and thickness on the stability and strength of the sheathed stud. It is found that higher density boards provided the greatest restraint against buckling, as did increased thicknesses. Isolating direct loading of the sheathing, and Composite action between the stud and sheathing are shown to be significant in determining the strength and controlling limit state of the stud.

In view of the problems of ineffective feature extraction and low detection accuracy in existing detection system, this study presents a novel machine vision-based approach composed of an ensemble of deep convolutional neural networks (CNNs) and improved Dempster-Shafer (D-S) theory-based data fusion to evaluate corrosion and coating defect of coal handling and preparation plants. To start with, the structural surface image is sent to each transferred CNN for initial defect identification. Then, an improved D-S fusion algorithm is proposed to combine the identification results from different CNNs, which are vectors consisting of statistical indicators of all the potential damage severity categories. The decision-level fusion of different CNNs can effectively improve image classification. To validate the performance of the proposed method, a dataset made of 3593 surface images with different defect severities captured from mining infrastructure in field is established together with data augmentation. The validation result demonstrates that the proposed method is able to effectively improve the recognition accuracy of defect severity and reduce the wrong recognition rate. Finally, the robustness of the proposed approach is also appraised by polluting the images with different types and intensities of noise, with satisfactory results.

Stainless steels are modern high-performance construction materials exhibiting excellent corrosion resistance, recyclability, ductility, and durability which make them appealing to use in the construction industry. However, when used as structural sections, they are subjected to localised failure in the web. This study aims to examine the structural behaviour of cold-formed low-carbon content standard austenitic 304L and 316L stainless steel channels under localised interior bearing loads. The results of 21 tests on unlipped channels with different cross-section sizes and thicknesses are presented. A nonlinear quasi-static Finite Element (FE) model is then developed. The FE model is validated against experimental test results and demonstrated good agreement in terms of bearing strength and failure modes. In addition, the experimental and FE results are used to compare the results against the results predicted in accordance with the American specification SEI/ASCE 8:2002 and European Standard EN 1993-1-4:2006. It is found that the current design equations are unreliable and too unconservative to use for cold-formed austenitic stainless steel unlipped channels, especially when compared to SEI/ASCE 8:2002, as much as 41%.

Strength serves as a vital performance metric for assessing long-term durability of cement-based materials. Nevertheless, there is a scarcity of models available for predicting residual strength of in-situ structures made of cement-based materials exposed to sulphate conditions. To address this challenge, this study presents a novel approach using deep learning to predict the degradation of compressive strength of cement-based materials under marine environments. Specifically, a deep convolutional neural network (DCNN) is established, consisting of two convolutional layers, one pooling layer, and two fully connected layers. In this innovative model, contents of cement, water-to-cement ratio, sand, sulphate concentration and exposure temperature are selected as inputs, while the output is strength of cement-based materials subjected to sulphate deterioration. To improve the forecast capability, particle swarm optimization is adopted for optimizing hyperparameters of DCNN, which can be implemented by reducing the discrepancy between model prediction and measured strength. Finally, experimental data are used to establish and evaluate proposed method. The results show that the proposed deep learning-based predictive model has the best performance of strength degradation prediction of cement-based materials suffering from sulphate attack via a comparison with other commonly used models. The outcome of this research offers a potential solution for predicting remaining strength of cement-based materials that undergo practical sulphate attack.

In this modern era of civilization, with the rise of construction of structures worldwide, there has been an increasing demand for structural health monitoring and assessment, which ensures the durability and stability of structural life. Terrestrial laser scanner (TLS) is one of those state-of-the-art technologies which has simplified the structural survey and assessment. TLS can rapidly acquire millions of 3D data points from an entire structural surface with high precision in a short period of time, encompassing the physical position and geometrical details of the subjected object in the form of a digital replica known as Digital Twin (DT). A Digital Twin is the set of all digitally represented data, which can greatly benefit engineers in terms of generating a realistic 3D model of the structure and identifying the structural deformities such as deformations, displacements, surface anomalies/irregularities, etc. Despite the significant benefit of this technology, there has been limited research studies on its application and evaluation in terms of accuracy in structural monitoring and assessment. Therefore, this research paper aims to provide an experimental program that not only measures and quantifies the performance of TLS, but also evaluates its accuracy in comparison to other conventional single-point measurement equipment. In this regard, a simply supported structural beam has been considered as a structural object and subjected to monotonic loading. Meanwhile, the deformation of the beam has been captured using a few methods including TLS. The finding of this research showed that the TLS performed remarkable well with errors less than 2% compared to other accurate approaches of measurements.