Search results

The capability of steel columns to support their design loads is highly affected by the time of exposure and temperature magnitude, which causes deterioration of mechanical properties of steel under fire conditions. It is known that structural steel loses strength and stiffness as temperature increases, particularly above 400 °C. The duration of time in which steel is exposed to high temperatures also has an impact on how much strength it loses. The time-dependent response of steel is critical when estimating load carrying capacity of steel columns exposed to fire. Thus, investigating the structural response of cold-formed steel (CFS) columns is gaining more interest due to the nature of such structural elements.

In this study, experiments were conducted on two CFS configurations: back-to-back (B-B) channel and toe-to-toe (T-T) channel sections. All CFS column specimens were exposed to different temperatures following the standard fire curve and cooled by air or water. A total of 14 tests were conducted to evaluate the capacity of the CFS sections. The axial resistance and yield deformation were noted for both section types at elevated temperatures. The CFS column sections were modelled to simulate the section's behaviour under various temperature exposures using the general-purpose finite element (FE) program ABAQUS. The results from FE modelling agreed well with the experimental results. Ultimate load of experiment and finite element model (FEM) are compared with each other. The difference in percentage and ratio between both are presented.

The results showed that B-B configuration showed better performance for all the investigated parameters than T-T sections. A noticeable loss in the ultimate strength of 34.5 and 65.6% was observed at 90 min (986℃) for B-B specimens cooled using air and water, respectively. However, the reduction was 29.9 and 46% in the T-T configuration, respectively.

This research paper focusses on assessing the buckling strength of heated CFS sections to analyse the mode of failure of CFS sections with B-B and T-T design configurations under the effect of elevated temperature.

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).

Concrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.

The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.

This study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

Investigate the fire performance of eccentrically loaded concrete partially encased column (PEC), using the advanced calculation method (ANSYS 18.2, 2017) and the simple calculation method in Annex G of Eurocode 4 (EN 1994-1-2, 2005). This work examines the influence of a range of parameters on fire behaviour of the composite column including: eccentricity loading, slenderness, reinforcement, fire rating and fire scenario. In this study, ISO-834 (ISO834-1, 1999) was used as fire source.

Currently, different methods of analysis used to assess the thermal behaviour of composite column exposed to fire. Analytical method named simplified calculation methods defined in European standard and numerical simulations named advanced calculation models are treated in this paper.

The load-bearing capacity of the composite column becomes very weak in the presence of the fire accident and eccentric loading, this recommends to avoid as much as possible eccentric loading during the design of construction building. The reinforcement has a slight influence on the temperature evolution; moreover, the reinforcement has a great contribution on the load capacity, especially in combined compression and bending. When only the two concrete sides are exposed to fire, the partially encased composite column presents a high load-bearing capacity value.

The use of a three-dimensional numerical model (ANSYS) allowed to describe easily the thermal behaviour of PEC columns under eccentric loading with the regard to the analytical method, which is based on three complex steps. In this study, the presence of the load eccentricity has found to have more effect on the load-bearing capacity than the slenderness of the composite column. Introducing a load eccentricity on the top of the column may have the same a reducing effect on the load-bearing capacity as the fire.

This study investigates the impact of the fire decay phase on structural damage using the sectional analysis method. The primary objective of this work is to forecast the non-dimensional capacity parameters for the axial and flexural load-carrying capacity of reinforced concrete (RC) sections for heating and the subsequent post-heating phase (decay phase) of the fire.

The sectional analysis method is used to determine the moment and axial capacities. The findings of sectional analysis and heat transfer for the heating stage are initially validated, and the analysis subsequently proceeds to determine the load capacity during the fire’s heating and decay phases by appropriately incorporating non-dimensional sectional and material parameters. The numerical analysis includes four fire curves with different cooling rates and steel percentages.

The study’s findings indicate that the rate at which the cooling process occurs after undergoing heating substantially impacts the axial and flexural capacity. The maximum degradation in axial and flexural capacity occurred in the range of 15–20% for cooling rates of 3 °C/min and 5 °C/min as compared to the capacity obtained at 120 min of heating for all steel percentages. As the fire cooling rate reduced to 1 °C/min, the highest deterioration in axial and flexural capacity reached 48–50% and 42–46%, respectively, in the post-heating stage.

The established non-dimensional parameters for axial and flexural capacity are limited to the analysed section in the study owing to the thermal profile, however, this can be modified depending on the section geometry and fire scenario.

The study primarily focusses on the degradation of axial and flexural capacity at various time intervals during the entire fire exposure, including heating and cooling. The findings obtained showed that following the completion of the fire’s heating phase, the structural capacity continued to decrease over the subsequent post-heating period. It is recommended that structural members' fire resistance designs encompass both the heating and cooling phases of a fire. Since the capacity degradation varies with fire duration, the conventional method is inadequate to design the load capacity for appropriate fire safety. Therefore, it is essential to adopt a performance-based approach while designing structural elements' capacity for the desired fire resistance rating. The proposed technique of using non-dimensional parameters will effectively support predicting the load capacity for required fire resistance.

The fire-resistant requirements for reinforced concrete structures are generally established based on standard fire exposure conditions, which account for the fire growth phase. However, it is important to note that concrete structures can experience internal damage over time during the decay phase of fires, which can be quantitatively determined using the proposed non-dimensional parameter approach.

In this article, we present an analysis of the axial compression behavior of thin circular tube columns filled with concrete. Experimental study was conducted by using one concrete composition and two lengths of columns. Thirteen columns were tested in axial compression. A comparison of experimental results with Algerian design codes DTR-DC, AIJ, DL/T and the empirical equations from the literature was performed. The analysis results for the thin tube show that the axial capacity in compression is underestimated by the DTR-DC. However, DL/T and AIJ codes are in agreement with test results. The results of empirical equations give different results depending on the length columns and section type.

Existing analytical methods for the evaluation of fire safety of reinforced concrete (RC) structures require extensive knowledge of heat transfer calculations and the finite element method. This paper aims to propose a rational method to predict the axial capacity of RC columns exposed to standard fire.

The average temperature distribution along the section height is first predicted for a specific fire scenario. The corresponding distribution of the reduced concrete strength is then integrated to develop expressions to calculate the axial capacity of RC columns exposed to fire from four faces.

These expressions provide structural engineers with a rational tool to satisfy the objective-based design clauses specified in the National Code of Canada in lieu of the traditional prescriptive methods.

The research is limited to standard fire curves and needs to be extended to cover natural fire curves.

This paper is the first to propose an accurate yet simple method to calculate the axial capacity of columns exposed to standard fire curves. The method can be applied using a simple Excel sheet. It can be further developed to apply to natural fire curves.

Concrete-filled double skin steel tubes (CFDST) columns are taken more attention due to their ability to withstand high structural loads in structures such as high-rise buildings, bridges' piers, offshore and marine structures. This paper is intended to improve the CFDST column's capacity without the need to increase the column's size to maintain its lightweight by filling it with self-compacted concrete (SCC) containing nanoclay (NC).

First, experimental investigation is conducted to select the optimal NC percentage that improves the mechanical properties. Different mixing method, mixture ingredients, cement content, and NC percentage are considered. Then, slender and short CFDST columns are tested for axial capacity to investigate the effect of adding the optimum NC percentage on column's capacity and failure mode.

The test results show that adding 3% NC by cement weight using dry mixing method to SCC is the optimum ratio. It is concluded that adding 3% NC by cement weight increased the CFDST column's capacity, especially the specimens with higher slenderness ratio. Moreover, it is concluded that more specimens should be tested under various geometric and reinforcement details.

Recently, CFDST tube columns solve many structural and architectural problems that engineers have encountered in traditional systems. Therefore, more studies are required to design high-performance columns capable of carrying complex loads with high efficiency since the traditional design could not achieve the required performance. Since concrete contributes to a large portion in the axial capacity of the CFDST columns, it is proposed to improve the CFDST column's capacity without the need to increase the column's size to maintain its lightweight by filling it with (SCC containing NC. Previous research has affirmed the effectiveness of employing nanoclay in the concrete's workability, durability, microstructures, and mechanical properties.

The purpose of this paper is to determine the failure progression resistance of the steel moment-resisting frames subjected to various beam-removal scenarios after application of the design earthquake pertinent to the structure by investigating a generic eight-story building.

The structure is first pushed to arrive at a target roof displacement corresponding to life safety level of performance. To simulate the post-earthquake beam-removal scenario, one of the beam elements is suddenly removed from the structure at a number of different positions. The structural response is then evaluated by using nonlinear static and dynamic analyses.

The results show that while no failure is observed in all of the scenarios, the vulnerability of the upper stories is much greater than that of the lower stories. In the next step, the structural resistance to such scenarios is determined. The results confirm that for the case study structure, at most, the resistance to failure progression in upper stories is 58 percent more than that of lower stories.

Failure and fracture of beam-to-column connections resulting in removal of beam elements may lead to a chain of subsequent failures in other structural members and eventually lead to progressive collapse in some cases. Deficiency in design or construction process of structures when combined by application of seismic loads may lead to such an event.

This study presents finite element (FE) and generalized regression neural network (GRNN) approaches for modeling multiple crack growth problems and predicting crack-growth directions under the influence of multiple crack parameters.

To determine the crack-growth direction in aluminum specimens, multiple crack parameters representing some degree of crack propagation complexity, including crack length, inclination angle, offset and distance, were examined. FE method models were developed for multiple crack growth simulations. To capture the complex relationships among multiple crack-growth variables, GRNN models were developed as nonlinear regression models. Six input variables and one output variable comprising 65 training and 20 test datasets were established.

The FE model could conveniently simulate the crack-growth directions. However, several multiple crack parameters could affect the simulation accuracy. The GRNN offers a reliable method for modeling the growth of multiple cracks. Using 76% of the total dataset, the NN model attained an R2 value of 0.985.

The models are presented for static multiple crack growth problems. No material anisotropy is observed.

In practical crack-growth analyses, the NN approach provides significant benefits and savings.

The proposed GRNN model is simple to develop and accurate. Its performance was superior to that of other NN models. This model is also suitable for modeling multiple crack growths with arbitrary geometries. The proposed GRNN model demonstrates its prediction capability with a simpler learning process, thus producing efficient multiple crack growth predictions and assessments.

The application of ultra-high performance concrete (UHPC) in anchorage zones can significantly improve the local compression performance of structures. However, the high cost and complex preparation of UHPC make UHPC difficult to be widely used in practice. This study proposes a method to strengthen the local compression zone of structures built by normal strength concrete (NSC) by incorporating UHPC cores.

In this study, a Finite Element Model (FEM) of local compression specimens was established by ABAQUS, and the accuracy of FEM was verified by comparing the FEM calculation results with experimental results. The verified FEM was adapted to the research on the influences of affecting factors on local compression performance of structures, including NSC strength, UHPC strength, spiral steel bar strength, and UHPC core diameter.

The results show that the peak load of the strengthened specimen SC1-U + N increases by 210.2% compared to that of the SC1-NSC. Furthermore, compared to SC1, the strengthened specimen SC1-U + N can save 64.7% amount of UHPC while the peak load decreases by only 34.4%. The peak load of the strengthened specimens increases with the axial compressive strength and the diameter of UHPC cores increasing, crack load increases with increasing the compressive strength of NSC, the spiral steel bar with high strength can prevent the sharp drop of load-deflection curve and the residual bearing capacity increases accordingly. All findings indicate that increasing the diameter of UHPC cores can improve the overall performance of the specimens. Under loading, all specimens fail by following a similar pattern. The effectiveness of this new strengthen method is also verified by FEM analytical calculations.

Based on the experimental study, this study extrapolates the influence of different parameters on the local bearing capacity of the strengthened specimens by finite element simulation. This method not only ensures the accuracy of bearing capacity assessment, but also does not require many samples, which ensures the economy of the reinforcement process. The research results provide a reference for the reinforcement design of anchorage zone.