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Progressive collapse because of high temperatures arising from an explosion, vehicle impact or fire is an important issue for structural failure in high-rise buildings.

The present study, using ABAQUS software for the analysis, investigated the progressive collapse of a two-dimensional, three-bay, four-storey steel frame structure from high-temperature stresses.

After structure reaches the temperature results like displacement, stress axial load and shear force are discussed.

Different temperatures were applied to the columns at different heights of a structure framed with various materials. Progressive collapse load combinations were also applied as per general service administration guidelines.

This study covered both steady-state and transient-state conditions of a multistorey-frame building subjected to a rise in temperature in the corner columns and intermediate columns. The columns in the framed structure were subjected to high temperatures at different heights, and the resulting displacements, stresses and axial loads were obtained, analysed and discussed.

The purpose of this study is progressive collapse behavior in buildings. It occurs due to removal/damage of a column by fire, blast or vehicle impact.

The present study investigates the comparative behavior of 3D four-storey moment resisting steel frame using ABAQUS to predict the sensitivity of the structure in progressive collapse because of fire loads. Columns at different levels were given different temperature with reduced material properties and yield strength. Progressive collapse load combination was adopted as per General Service Administration guidelines. Corner, middle, intermediate, multiple corner and multiple intermediate columns were subjected to fire load separately.

The results for displacement, stress, shear force and axial force were captured and discussed.

The study covers linear analysis of steel frame because of different temperature. In linear analysis. columns were subjected to different temperature and their results were studied. Effect of temperature in the structure were captured because of different fire conditions.

Lean construction (LC) is an innovative approach in the construction industry that has shown significant success in developed countries. Although LC has potential in the construction sector of Pakistan, it has not been extensively explored. This study aims to address this knowledge gap by identifying and predicting current lean practices and assessing the strengths and weaknesses of LC implementation in Pakistan.

Using robust statistical methods to analyze 92 valid responses, the study reveals that approximately 54% of lean practices are currently in use in the construction industry of Pakistan, with a population mean ranging from 52.7% to 55.6%.

Surprisingly, the research identifies instances where some construction firms in Pakistan are implementing LC practices, even though they have only a limited understanding of its underlying principles. Notably, certain subprinciples, such as visual management, top management commitment to change, employee training, process cycle time reduction and production optimization, are less integrated within the construction industry. Exploring the possibility of implementing LC, recommendations for strategies to implement LC in Pakistan are suggested, aligning with the conceptual model proposed by the researchers.

The novelty of this work offers insights that can serve as a comprehensive guide for developing nations. It provides a structured approach to assess and benchmark LC practices, which, in turn, can contribute to a more efficient and effective construction industry. Moreover, the strategies proposed in this research can aid developing countries in the efficient implementation of LC. This will have a positive implication for both economic and developmental outcomes.

The purpose of this study is to investigate the influence of progressive collapse under high temperature for a reinforced concrete (RC) frame. An analytical programme was analysed for a two-bay five-storey RC frame exposed to high temperature at different column locations.

The effects of high temperature protections and locations (i.e. corner, middle and intermediate) on collapse conditions and load distributions were studied for the steady-state linear analysis using finite element software.

The results show that the frame will not collapse suddenly at temperatures up to 400°C. This is attributed to an increase in the deflections of the column, which increases the lateral displacement of adjacent heated columns and governs their buckling. This indicates that the temperature rating in the column against collapse can occur at a range of 500°C-600°C compared to that of individual members. The collapse pattern of RC frames designed as ordinary moment resisting frames, and under ordinary load, combinations is based on GSA guidelines. The results for displacement, stress and axial force were collected and discussed.

The two-bay five-storey frame has been created in finite element software, and linear analysis is used to perform this study with a different temperature.

In this paper the use of classical strain measures in analysis of trusses at finite deformations will be discussed. The results will be compared to the ones acquired using a novel strain measure based on the Hyperbolic Sine function. Through the evaluation of results, algebraic development and graph analysis, the properties of the Hyperbolic Sine strain measure will be examined.

Through graph plotting, comparisons between the novel strain measure and the classic ones will be made. The formulae for the implementation of the Hyperbolic Sine strain measure into a positional finite element method are developed. Four engineering applications are presented and comparisons between results obtained using all strain measures studied are made.

The proposed strain measure, Hyperbolic Sine, has objectivity and symmetry. The linear constitutive model formed by the Hyperbolic Sine strain and its conjugated stress presents an increasing stiffness, both in compression and tension, a behavior that can be useful in the modeling of several materials.

The structural analysis performed on the four examples of trusses in this article did not consider the variation of the cross-sectional area of the elements or the buckling phenomenon, moreover, only elastic behavior is considered.

The present article proposes the use of a novel strain measure family, based on the Hyperbolic Sine function and suitable for structural applications. Mathematical expressions for the use of the Hyperbolic Sine strain measure are established following the energetic concepts of the positional formulation of the finite element method.

At this moment, there is substantial anxiety surrounding the fire safety of huge reinforced concrete (RC) constructions. The limitations enforced by test facilities, technology, and high costs have significantly limited both full-scale and scaled-down structural fire experiments. The behavior of an individual structural component can have an impact on the entire structural system when it is connected to it. This paper addresses the development and testing of a self-straining preloading setup that is used to perform thermomechanical action in RC beams and slabs.

Thermomechanical action is a combination of both structural loads and a high-temperature effect. Buildings undergo thermomechanical action when it is exposed to fire. RC beams and slabs are one of the predominant structural members. The conventional method of testing the beams and slabs under high temperatures will be performed by heating the specimens separately under the desired temperature, and then mechanical loading will be performed. This gives the residual strength of the beams and slabs under high temperatures. This method does not show the real-time behavior of the element under fire. In real-time, a fire occurs simultaneously when the structure is subjected to desired loads and this condition is called thermomechanical action. To satisfy this condition, a unique self-training test setup was prepared. The setup is based on the concept of a prestressing condition where the load is applied through the bolts.

To validate the test setup, two RC beams and slabs were used. The test setup was tested in service load range and a temperature of 300 °C. One of the beams and slabs was tested conventionally with four-point bending and point loading on the slab, and another beam and slab were tested using the preloading setup. The results indicate the successful operation of the developed self-strain preloading setup under thermomechanical action.

Gaining insight into the unpredictable reaction of structural systems to fire is crucial for designing resilient structures that can withstand disasters. However, comprehending the instantaneous behavior might be a daunting undertaking as it necessitates extensive testing resources. Therefore, a thorough quantitative and qualitative numerical analysis could effectively evaluate the significance of this research.

The study was performed to validate the thermomechanical load setup for beams and slabs on a single-bay single-storey RC frame with and without slab under various fire possible scenarios. The thermomechanical load setup for RC members is found to be scarce.

This study aims to investigate factors leading to homebuyer complaints during defect liability periods (DLP). The specific study objectives are to: identify critical factors leading to homebuyer complaints during DLP; compare the factors among regions, house price and developer recognition; group the factors into subcategories; and evaluate the criticality of the categories.

A systematic literature review and semi-structured interviews with 20 homebuyers were carried out, generating 37 factors. A survey was developed with the factors categorized into three categories: individual presumptions, defects discovery and post-rectifications. The survey data were collected from 104 homebuyers who had acquired new houses within the last five years. The collected data were subjected to statistical analyses, including normalized mean analysis, Kruskal–Wallis H test, factor analysis and fuzzy synthetic evaluation.

The results indicate that individual presumptions, defects discovery and post-rectifications have 8, 14 and 6 critical factors. Then, there are high similarities in the criticality of the factors among regions, house price and developer recognition. The defect discovery factors can be grouped into function- and precision-related factors. Finally, the defect discovery category has the highest overall criticality, followed by post-rectification and individual presumption categories.

To the best of the authors’ knowledge, this study is the first to quantitatively investigate factors leading to homebuyers’ complaints during DLP. The study findings offer a new perspective for policymakers in the development of housing regulations.

The purpose of this study is to perform a numerical analysis of fiber-reinforced polymer (FRP) retrofitting in reinforced concrete (RC) joints at high temperatures and predict models using artificial neural networks (ANN). The aim was to gain insights into their structural behavior across a range of loading conditions from room temperature to 800°C. Additionally, the research assessed the efficiency of carbon fiber-reinforced polymer (CFRP), glass fiber reinforced polymer (GFRP) and aramid fiber reinforced polymer (AFRP) strengthening in enhancing the structural performance of the critical sections.

The linear numerical simulations were conducted to evaluate the performance of RC beam-column joints using finite element modelling (FEM) analysis. The ANN model demonstrated exceptional effectiveness in predicting the stiffness of frames with openings, establishing itself as the premier machine learning algorithm for frame stiffness estimation. In the conventional model, 300°C was proven to be an effective temperature approach. Subsequently, maintaining a constant temperature of 300°C, an in-depth analysis of nearly 30 models of three retrofitting techniques was conducted under thermomechanical loading.

The CFRP retrofits yielded 15% less deflection and 30% more stress than the remaining FRPs, and the ANN models predicted the deflection, main stresses, bending moment and shear force. The ANN model results were compared with those of other frequently used models. The R thresholds (R = 0.954, 0.981, 0.986, 0.968, 0.978 and 0.936) for training, testing and validation indicated that the ANN model achieved data variability. The findings indicate that the ANN model is more accurate because of the strong connection between the numerical model and the prediction.

To identify the pinpoint of critical segments within the rehabilitation section and determine the most effective wrapping method among the three laminates.