• Steel and Composite Structures
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• v.47 no.2
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• pp.269-287
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• 2023

The WGJ420 fire-resistant weathering (FRW) steel is developed and manufactured with standard yield strength of 420 MPa at room temperature, which is expected to significantly enhance the performance of steel structures with excellent fire and corrosion resistances, strong seismic capacity, high strength and ductility, good resilience and robustness. In this paper, the mechanical properties of FRW steel plates and buckling behavior of columns are investigated through tests at elevated temperatures. The stress-strain curves, mechanical properties of FRW steel such as modulus of elasticity, proof strength, tensile strength, as well as corresponding reduction factors are obtained and discussed. The recommended constitutive model based on the Ramberg-Osgood relationship, as well as the relevant formulas for mechanical properties are proposed, which provide fundamental mechanical parameters and references. A total of 12 FRW steel welded I-section columns with different slenderness ratios and buckling load ratios are tested under standard fire to understand the global buckling behavior in-depth. The influences of boundary conditions on the buckling failure modes as well as the critical temperatures are also investigated. In addition, the temperature distributions at different sections/locations of the columns are obtained. It is found that the buckling deformation curve can be divided into four stages: initial expansion stage, stable stage, compression stage and failure stage. The fire test results concluded that the residual buckling capacities of FRW steel columns are substantially higher than the conventional steel columns at elevated temperatures. Furthermore, the numerical results show good agreement with the fire test results in terms of the critical temperature and maximum axial elongation. Finally, the critical temperatures between the numerical results and various code/standard curves (GB 51249, Eurocode 3, AS 4100, BS 5950 and AISC) are compared and verified both in the buckling resistance domain and in the temperature domain. It is demonstrated that the FRW steel columns have sufficient safety redundancy for fire resistance when they are designed according to current codes or standards.

• Advances in Computational Design
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• v.7 no.4
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• pp.321-343
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• 2022

Museum buildings display artefacts for public education and enjoyment, ensuring their long-term safety and the comfort of visitors by following strict indoor environment control protocols using mechanical Heating, Ventilation and Air Conditioning (HVAC) systems to keep the (environmental) variables at a fixed comfort level. Maintaining this requires constant supply of energy currently mostly sourced from the combustion of fossil fuels which exacerbates climate change. However, a review on the effects of the indoor environmental variables on museum artefacts as well as museum visitors revealed that there is no specific point at which artefact deterioration occurs, and that there are wide ranges of conditions that guarantee the long-term safety of artefacts and human comfort. Visits to museum buildings in hot-humid tropical climate of Nigeria revealed that strict indoor environmental practices were adopted. Even when appropriate micro-climatic conditions are provided for artefacts, mechanical HVAC systems remain necessary for visitor comfort because almost no consideration is given to natural ventilation. With the current global push towards energy management, this paper reviewed passive environmental control practices, architectural design strategies, and discusses the adaptation of double skin façade with jali screens, and the notion of smart materials, which can satisfy the range of requirements for the long-term safety of artefacts and levels of human comfort in buildings in hot-humid tropical climate, without mechanical HVAC systems. This review would inspire more discussions on passive, energy efficient, smart and climate responsible popular architecture, challenging current thinking on the impact of the more accepted representative architecture.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• Structural Engineering and Mechanics
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• v.84 no.4
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• pp.489-502
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• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.

• Steel and Composite Structures
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• v.49 no.2
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• pp.143-159
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• 2023

Recently, the design of scaffolding systems has garnered considerable attention due to the increasing number of scaffold collapses. These incidents arise from the underestimation of imposed loads and the site-specific conditions that restrict the application of lateral restraints in scaffold assemblies. The present study is committed to augmenting the buckling resistance of vertical support members, obviating the need for supplementary lateral restraints. To achieve this objective, experimental and computational analyses were performed to assess the axial load buckling capacity of steel props, composed of two hollow steel pipes that slide into each other for a certain length. Three full-scale steel props with various geometric properties were tested to construct and validate the analytical models. The total unsupported length of the steel props is 6 m, while three pins were installed to tighten the outer and inner pipes in the distance they overlapped. Finite Element (FE) modeling is carried out for the three steel props, and the developed models were verified using the experimental results. Also, theoretical analysis is utilized to verify the FE analysis. Using the FE-verified models, a parametric study is conducted to evaluate the effect of different inserted pipe lengths on the steel props' axial load capacity and lateral displacement. Based on the results, the typical failure mode for the studied steel props is global elastic buckling. Also, the prop's elastic buckling strength is sensitive to the inserted length of the smaller pipe. A threshold of minimum inserted length is one-third of the total length, after which the buckling strength increases. The present study offers a prop with enhanced buckling resistance and introduces an equation for calculating an equivalent effective length factor (k), which can be seamlessly incorporated into Euler's buckling equation, thereby facilitating the determination of the buckling capacity of the enhanced props and providing a pragmatic engineering solution.

• Geomechanics and Engineering
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• v.36 no.1
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• pp.27-37
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• 2024

The roof-filling body system stability plays a key role in gob-side entry retained (GER). Taking the GER of the 1103 belt transportation roadway in Heilong Coal Mine as engineering background, stability analysis of roof-filling body system was conducted based on the cusp catastrophe theory. Theoretical results showed that the current design parameters of 1103 belt transportation roadway could ensure the roof-filling body system stable during the resistance-increasing support stage of the filling body and the stable support stage of the filling body. Moreover, a verified global numerical model in FLAC3D was established to analyze the failure characteristics including surrounding rock deformation, stress distribution, and plastic zone. Numerical simulation indicated that the width-height ratio of the filling body had a great influence on the stability of the roof-filling body system. When the width-height ratio was greater than 0.62, with the decrease of the width-height ratio, the peak stress of the filling body gradually decreased; when the width-height ratio was greater than 0.92, as the distance to the roadway increased, the roof stress increased and then decreased. The theoretical analysis and numerical simulation findings in this study provide a new research method to analyze the stability of the roof-filling body system in GER.

• Membrane and Water Treatment
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• v.14 no.4
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• pp.155-162
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• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.