Search results

Simulations exist for the prediction of the behaviour of building structural systems under fire, including two-way coupled fire-structure interaction. However, these simulations do not include detailed models of the connections, whereas these connections may impact the overall behaviour of the structure. Therefore, this paper proposes a two-scale method to include screw connections.

The two-scale method consists of (a) a global-scale model that models the overall structural system and (b) a small-scale model to describe a screw connection. Components in the global-scale model are connected by a spring element instead of a modelled screw, and the stiffness of this spring element is predicted by the small-scale model, updated at each load step. For computational efficiency, the small-scale model uses a proprietary technique to model the behaviour of the threads, verified by simulations that model the complete thread geometry, and validated by existing pull-out experiments. For four screw failure modes, load-deformation behaviour and failure predictions of the two-scale method are verified by a detailed system model. Additionally, the two-scale method is validated for a combined load case by existing experiments, and demonstrated for different temperatures. Finally, the two-scale method is illustrated as part of a two-way coupled fire-structure simulation.

It was shown that proprietary ”threaded connection interaction” can predict thread relevant failure modes, i.e. thread failure, shank tension failure, and pull-out. For bearing, shear, tension, and pull-out failure, load-deformation behaviour and failure predictions of the two-scale method correspond with the detailed system model and Eurocode predictions. Related to combined load cases, for a variety of experiments a good correlation has been found between experimental and simulation results, however, pull-out simulations were shown to be inconsistent.

More research is needed before the two-scale method can be used under all conditions. This relates to the failure criteria for pull-out, combined load cases, and temperature loads.

The two-scale method bridges the existing very detailed small-scale screw models with present global-scale structural models, that in the best case only use springs. It shows to be insightful, for it contains a functional separation of scales, revealing their relationships, and it is computationally efficient as it allows for distributed computing. Furthermore, local small-scale non-convergence (e.g. a screw failing) can be handled without convergence problems in the global-scale structural model.

The aim of this paper is to determine the thermal conductivity of a protective layer of alkali-activated cement and the possibility of performing fire protection with fireclay sand and Lightweight mortar. Unprotected steel structures have generally low fire resistance and require surface protection. The design of passive protection of a steel element must consider the service life of the structure and the possible need to replace the fire protection layer. Currently, conventional passive protection options include intumescent coatings, which are subject to frequent inspection and renewal, gypsum and cement-based fire coatings and gypsum and cement board fire protection.

Alkali-activated cements provide an alternative to traditional Portland clinker-based materials for specific areas. This paper presents the properties of hybrid cement, its manufacturability for conventional mortars and the development of passive fire protection. Fire experiments were conducted with mortar with alkali-activated and fireclay sand and lightweight mortar with alkali-activated cement and expanded perlite. Fire experiment FE modelling.

The temperatures of the protected steel and the formation of cracks in the protective layer were investigated. Based on the experiments, the thermal conductivities of the two protective layers were determined. Conclusions are presented on the applicability of alkaline-activated cement mortars and the possibilities of applicability for the protection of steel structures. The functionality of the passive fire layer was confirmed and the strengths of the mortar used were determined. The use of alkali-activated cements was shown to be a suitable option for sustainable passive fire protection of steel structures.

Eco-friendly fire protection based on hybrid alkali-activated cement of steel members.

The construction industries at present are focusing on designing sustainable concrete with less carbon footprint. Considering this aspect, a Fibre-Reinforced Geopolymer Concrete (FGC) was developed with 8 and 10 molarities (M). At elevated temperatures, concrete experiences deterioration of its mechanical properties which is in some cases associated with spalling, leading to the building collapse.

In this study, six geopolymer-based mix proportions are prepared with crimped steel fibre (SF), polypropylene fibre (PF), basalt fibre (BF), a hybrid mixture consisting of (SF + PF), a hybrid mixture with (SF + BF), and a reference specimen (without fibres). After temperature exposure, ultrasonic pulse velocity, physical characteristics of damaged concrete, loss of compressive strength (CS), split tensile strength (TS), and flexural strength (FS) of concrete are assessed. A polynomial relationship is developed between residual strength properties of concrete, and it showed a good agreement.

The test results concluded that concrete with BF showed a lower loss in CS after 925 °C (i.e. 60 min of heating) temperature exposure. In the case of TS, and FS, the concrete with SF had lesser loss in strength. After 986 °C and 1029 °C exposure, concrete with the hybrid combination (SF + BF) showed lower strength deterioration in CS, TS, and FS as compared to concrete with PF and SF + PF. The rate of reduction in strength is similar to that of GC-BF in CS, GC-SF in TS and FS.

Performance evaluation under fire exposure is necessary for FGC. In this study, we provided the mechanical behaviour and physical properties of SF, PF, and BF-based geopolymer concrete exposed to high temperatures, which were evaluated according to ISO standards. In addition, micro-structural behaviour and linear polynomials are observed.

Even though it is widely used, reinforced concrete (RC) is susceptible to damage from various environmental factors. The hazard of a fire attack is particularly severe because it may cause the whole structure to collapse. Furthermore, repairing and strengthening existing structures with high-performance concrete (HPC) has become essential from both technical and financial points of view. In particular, studying the postfire behavior of HPC with normal strength concrete substrate requires experimental and numerical investigations. Accordingly, this study aims to numerically investigate the post-fire behavior of reinforced composite RC slabs.

Consequently, in this study, a numerical analysis was carried out to ascertain the flexural behavior of simply supported RC slabs strengthened with HPC and exposed to a particularly high temperature of 600°C for 2 h. This behavior was investigated and analyzed in the presence of a number of parameters, such as HPC types (fiber-reinforced, 0.5% steel, polypropylene fibers [PPF], hybrid fibers), strengthening side (tension or compression), strengthening layer thickness, slab thickness, boundary conditions, reinforcement ratio and yield strength of reinforcement.

The results showed that traction-separation and full-bond models can achieve accuracy compared with experimental results. Also, the fiber type significantly affects the postfire performance of RC slab strengthened with HPC, where the inclusion of hybrid fiber recorded the highest ultimate load. While adding PPF to HPC showed a rapid decrease in the load-deflection curve after reaching the ultimate load.

The proposed model accurately predicted the thermomechanical behavior of RC slabs strengthened with HPC after being exposed to the fire regarding load-deflection response, crack pattern and failure mode. Moreover, the considered independent parametric variables significantly affect the composite slabs’ behavior.

This paper aims to examine the fire retardant property potentials of cow horn ash particles (CHAp) bio-additive and aluminium trihydrate (AH), a traditional inorganic fire-retardant additive, respectively, in banana peduncle fibre (BPF) reinforced polyester composites. An attempt was made to comparatively analyse the fire retardant capacity potentials of CHAp, a bio-material waste that is readily available, at no cost, as a potential fire retardant material for composites manufacture with a conventional inorganic fire retardant additive (AH).

The fibre used in this research was derived from the banana peduncle. The matrix is unsaturated polyester. A scanning electron microscope was used to analyze the particle size of the carbonized CHAp. The composites were compounded using 0%, 2.5%, 5%, 7.5% and 10% of CHAp and AH, respectively. A cone calorimeter instrument was used in the analysis to obtain combustion information of CHAp and AH formulated polyester-BPF composites. Test samples were cut to the dimensions of 100 × 100 mm. All materials are conditioned at 23 ± 30 °C and the relative humidity of 50 ± 5% for 24 h before testing. The samples were wrapped with aluminium foil around the back and edges before placing the samples on the holder and then into the cone calorimeter. The samples were backed with a non-combustible insulating refractory material (brick). The samples were orientated horizontally and exposed to irradiances of 50 kW/m² at a temperature of approximately 6000 °C. The samples were pilot ignited and ran in triplicate; the average readings of the three runs were taken.

The results obtained from the analysis depicted similar fire retardant properties for formulations with CHAp and AH, respectively. Composites formulated with CHAp exhibited delayed ignition time of 25%, increased end of burning time of 14.24% and reduced total heat release rate of 9.07% for the developed composites. The developed BPF/CHAp/polyester composites yield composites with fire retardancy, which would find relevance in the engineering material industry.

CHAp, therefore, would suffice as an alternative to the inorganic, expensive and non-environmental friendly, conventional fire retardant additives used in composites manufacture.

Elevated temperature material properties are essential in predicting structural member's behavior in high-temperature exposures such as fire. Even though experimental methodologies are available to determine these properties, advanced equipment with high costs is required to perform those tests. Therefore, performing those experiments frequently is not feasible, and the development of numerical techniques is beneficial. A numerical technique is proposed in this study to determine the temperature-dependent thermal properties of the material using the fire test results based on the Artificial Neural Network (ANN)-based Finite Element (FE) model.

An ANN-based FE model was developed in the Matlab program to determine the elevated temperature thermal diffusivity, thermal conductivity and the product of specific heat and density of a material. The temperature distribution obtained from fire tests is fed to the ANN-based FE model and material properties are predicted to match the temperature distribution.

Elevated temperature thermal properties of normal-weight concrete (NWC), gypsum plasterboard and lightweight concrete were predicted using the developed model, and good agreement was observed with the actual material properties measured experimentally. The developed method could be utilized to determine any materials' elevated temperature material properties numerically with the adequate temperature distribution data obtained during a fire or heat transfer test.

Temperature-dependent material properties are important in predicting the behavior of structural elements exposed to fire. This research study developed a numerical technique utilizing ANN theories to determine elevated temperature thermal diffusivity, thermal conductivity and product of specific heat and density. Experimental methods are available to evaluate the material properties at high temperatures. However, these testing equipment are expensive and sophisticated; therefore, these equipment are not popular in laboratories causing a lack of high-temperature material properties for novel materials. However conducting a fire test to evaluate fire performance of any novel material is the common practice in the industry. ANN-based FE model developed in this study could utilize those fire testing results of the structural member (temperature distribution of the member throughout the fire tests) to predict the material's thermal properties.

The purpose of this paper is to describe an innovative approach to overcoming a common dilemma in designing negotiation simulations – that of situating a simulation in a real-life or fictitious context. This binary choice, which the authors call the negotiation designer’s dilemma, has profound implications for the types of learning activities and outcomes that can be integrated into the overall learning experience. As a way of overcoming the trade-offs inherent in this dilemma, the authors developed what they term hybrid simulations, which blend elements of fact and fiction in its contextual design in a particular way.

The authors were part of a negotiation simulation design team that used Design Thinking to understand the negotiation designer’s dilemma and to prototype and test a corresponding solution.

This paper demonstrates the benefits, potential applications and the how-to of hybrid simulations within the context of two such simulations the authors have designed at two different Swiss business schools. This paper concludes by discussing the potential and limitations for the application of hybrid simulations, as well as areas of potential further development.

The concept of a hybrid negotiation is a novel design trick that can be used in a variety of negotiation simulation contexts.

Intumescent coatings are nowadays a dominant passive system used to protect structural materials in case of fire. Due to their reactive swelling behaviour, intumescent coatings are particularly complex materials to be modelled and predicted, which can be extremely useful especially for performance-based fire safety designs. In addition, many parameters influence their performance, and this challenges the definition and quantification of their material properties. Several approaches and models of various complexities are proposed in the literature, and they are reviewed and analysed in a critical literature review.

Analytical, finite-difference and finite-element methods for modelling intumescent coatings are compared, followed by the definition and quantification of the main physical, thermal, and optical properties of intumescent coatings: swelled thickness, thermal conductivity and resistance, density, specific heat capacity, and emissivity/absorptivity.

The study highlights the scarce consideration of key influencing factors on the material properties, and the tendency to simplify the problem into effective thermo-physical properties, such as effective thermal conductivity. As a conclusion, the literature review underlines the lack of homogenisation of modelling approaches and material properties, as well as the need for a universal modelling method that can generally simulate the performance of intumescent coatings, combine the large amount of published experimental data, and reliably produce fire-safe performance-based designs.

Due to their limited applicability, high complexity and little comparability, the presented literature review does not focus on analysing and comparing different multi-component models, constituted of many model-specific input parameters. On the contrary, the presented literature review compares various approaches, models and thermo-physical properties which primarily focusses on solving the heat transfer problem through swelling intumescent systems.

The presented literature review analyses and discusses the various modelling approaches to describe and predict the behaviour of swelling intumescent coatings as fire protection for structural materials. Due to the vast variety of available commercial products and potential testing conditions, these data are rarely compared and combined to achieve an overall understanding on the response of intumescent coatings as fire protection measure. The study highlights the lack of information and homogenisation of various modelling approaches, and it underlines the research needs about several aspects related to the intumescent coating behaviour modelling, also providing some useful suggestions for future studies.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.

The major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.

The performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.

The JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.

The proposed Covid-19 detection method is useful in various applications, like medical and so on.

Developed JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.