Equal angle sections used as columns exhibit bending or buckling behavior under axial loads, influenced by their shape and potential for uneven load distribution. Their performance is significantly affected by factors such as slenderness ratio, boundary conditions, and alignment during construction. This paper presents findings from experiments aimed at determining the maximum load capacity of equal-angle sections used as column members. The experiments were conducted on 12 different hot-rolled steel equal-angle sections with both ends hinged. To validate the experimental results regarding ultimate load and failure mode, a nonlinear finite element model was developed in ABAQUS. The results of this parametric study were compared with the code standards IS 800: 2007 and BS 5950–1: 2000. Furthermore, machine learning (ML) techniques were employed to predict the ultimate loads of hot-rolled steel equal-angle sections. ML models, including artificial neural networks (ANN) and gradient boosting regression (GBR), were created to forecast the ultimate load based on finite element analysis results. Several statistical metrics were employed to assess the accuracy of these models in predicting the ultimate load. This study highlights the effectiveness of ML techniques, showing that ANN and GBR are the most reliable forecasting methods for analyzing the ultimate load of hot-rolled steel angle sections.

Hot-rolled steel (HRS) is a fundamental material in structural engineering, notable for its mechanical properties and diverse applications. This study focused on I-sections (ISMB) of varying dimensions, conducting both experimental and numerical investigations. A nonlinear finite element model (FEM) was developed using ABAQUS to validate the experimental results regarding axial capacity and failure modes. The FEM results of the parametric study were subsequently compared against the IS 800:2007 and BS 5950-1:2000 code specifications. Additionally, this study investigated the application of machine learning methods to predict the axial capacity of HRS sections. Specifically, soft computing models such as Artificial Neural Networks (ANN), Gradient Tree Boosting (GTB), and Multivariate Adaptive Regression Splines (MARS) were developed to predict the axial capacity of HRS sections based on the findings of finite element analysis. Comparison of the predicted results with experimental observations demonstrated the reliability and robustness of these machine learning models in approximating the axial capacity of hot-rolled steel columns. This suggests that these soft computing models can be effective tools for predicting the ultimate strength or axial capacity of ISMB sections.

Rapid technological advancement is underway in the sphere of material science research. Several studies have been undertaken around the globe over the last four decades to improve the strength and durability performance of concrete. As a result of ongoing research and experimentation, concrete no longer just consists of the traditional materials of cement, aggregates and water but has transformed into an engineered custom material with efficient new ingredients in order to meet the demands of the expanding construction industry. In this experimental study, biomedical waste incinerator ash (BMIA) was employed as a partial substitute for cement in self-compacting concrete (SCC), designed for M30 grade. BMIA was partially replaced with cement in proportions of 0%, 5%, 10%, 15% and 20% by cement weight. This experimental work aimed to study the fresh, mechanical and durability characteristics of the SCC mixes incorporating BMIA. A suitable super-plasticizer was used to retain the rheological qualities of fresh concrete. To investigate the mechanical and durability characteristics, experiments on hardened concrete were performed. The results demonstrate that 5% of BMIA substitution for cement in the SCC mix had higher strength compared to all other mixes because BMIA’s fine particles filled the voids in the hardened concrete. Scanning electronic microscopy (SEM) and X-ray diffraction (XRD) analyses were performed to examine the microstructure of BMIA substituted SCC versus conventional SCC mix. The chemical composition test revealed that BMIA can be employed in the SCC mix up to 5% efficiently, which will result in waste utilisation and disposal.

Abstract Self-compacting Concrete (SCC) is a special form of high-performance concrete that is highly efficient in its filling, flowing, and passing abilities. In this study, an attempt has been made to model the compressive strength (CS) of SCC mixes using machine learning approaches. The SCC mixes were designed considering light-weight expandable clay aggregate (LECA) as a partial replacement for coarse aggregate, ground granulated blast furnace slag (GGBS) as a partial replacement for binding material (cement), and incinerated bio-medical waste ash (IBMWA) as a partial replacement for fine aggregate. LECA, GGBS, and IBMWA were replaced with coarse aggregate, cement, and fine aggregate at different substitution levels of 10%, 20%, and 30%. The M30 grade SCC mixes were designed for two different water/binder ratios – 0.40 and 0.45 and the CS of SCC mixes was experimentally determined along with the fresh state properties assessed by slump flow, L-box, J-ring, and V-funnel tests. The CS of SCC mixes so obtained from the experimental analysis was considered for machine learning (ML) based modeling using paradigms such as Artificial Neural Network (ANN), Gradient Tree Boosting (GTB), and Cat-Boost Regressor (CBR). The ML models were developed considering the compressive strength of SCC as the target parameter. The quantities of materials (in terms of %), water to binder ratio, and density of SCC specimens were used as input variables to simulate the ML models. The results from the experimental analysis show that the optimum replacement percentages for cement, coarse, and fine aggregates were 30%, 10%, and 20%, respectively. The ML models were successful in modeling the compressive strength of SCC mixes with higher accuracy and the least errors. The CBR model performed relatively better than the other two ML models, with relatively higher efficiency (KGE=0.9671) and the least error (mean absolute error = 0.52) during the testing phase.

Self-compacting concrete (SCC) is a special form of high-performance concrete that is highly efficient in its filling, flowing, and passing abilities. In this study, an attempt has been made to model the compressive strength (CS) of SCC mixes using machine-learning approaches. The SCC mixes were designed considering lightweight expandable clay aggregate (LECA) as a partial replacement for coarse aggregate; ground granulated blast-furnace slag (GGBS) as a partial replacement for binding material (cement); and incinerated bio-medical waste ash (IBMWA) as a partial replacement for fine aggregate. LECA, GGBS, and IBMWA were replaced with coarse aggregate, cement, and fine aggregate, respectively at different substitution levels of 10%, 20%, and 30%. M30-grade SCC mixes were designed for two different water/binder ratios—0.40 and 0.45—and the CS of the SCC mixes was experimentally determined along with the fresh state properties assessed by slump-flow, L-box, J-ring, and V-funnel tests. The CS of the SCC mixes obtained from the experimental analysis was considered for machine learning (ML)-based modeling using paradigms such as artificial neural networks (ANN), gradient tree boosting (GTB), and CatBoost Regressor (CBR). The ML models were developed considering the compressive strength of SCC as the target parameter. The quantities of materials (in terms of %), water-to-binder ratio, and density of the SCC specimens were used as input variables to simulate the ML models. The results from the experimental analysis show that the optimum replacement percentages for cement, coarse, and fine aggregates were 30%, 10%, and 20%, respectively. The ML models were successful in modeling the compressive strength of SCC mixes with higher accuracy and the least errors. The CBR model performed relatively better than the other two ML models, with relatively higher efficiency (KGE = 0.9671) and the least error (mean absolute error = 0.52 MPa) during the testing phase.

The construction industry is growing rapidly all over the world and busy in inventing the new construction materials. The existing construction materials which are used for constructions from past decades were accumulating on the land as a waste, due to lack of reutilizing the used materials. In this experimental work, efforts were made to re-utilize the recycled aggregates which are also called demolition waste aggregates. The current study is divided into two stages. The first stage of work examines the influence of recycled aggregates on the characteristics of self-compacting concrete (SCC). The second stage includes the substitution of cement by nano silica and replacement of normal aggregates by recycled aggregates (RA) in the SCC mix. In this study, natural aggregates are replaced by 10%, 20%, 35% and 50% with recycled aggregates by the weight of total aggregates. Nano silica (NS) is used in this study, which replaces the 0.5% and 1% by weight of cement for each partial replacement level of recycled aggregates. The workability tests were performed on prepared SCC mix. The workability properties were satisfying the requirements of SCC. The compressive, split tensile and flexural strength tests were conducted on hardened concrete. The microstructural configuration of SCC containing with and without RA & NS mixtures were examined using a scanning electronic microscope (SEM). The RCPT test was conducted to understand the durability of the SCC. The outcomes of mechanical properties were optimum at 35% replacement of RA in first stage of work. Similarly, mechanical properties were optimum at 1% of NS and 35% of RA in second stage of work. The SEM analysis reveals the micro structure of mixes, the nano silica replaced concrete mix exhibit the lesser voids compared to RA replaced concrete. It results shows that voids were filled with nano particles and enhances the performance of SCC mix.

In present times, the utilization of cold formed steel (CFS) members in building industry is rapidly growing on account of its higher strength-to-weight ratio and the significant flexibility of shapes and size accessible to the structural steel engineer. Beside these advantages, it has also some desirable properties which create more challenges during its use in construction. As the thickness is very small it can easily get affected by different types of buckling, torsional failure, web crippling, and have low resistance to fire. The key focus of this study is to represent the CFS members by reviewing its properties, classification, buckling modes, etc. The paper discusses the codes and guidelines available for cold-formed steel sections and different analysis methods carried out by many researchers. At present time, the application of modernized finite element method in CFS will permit the development of creative and structured building products. The selection of the best section is a great challenge and also expensive, the use of artificial neural network will predict the strength of the sections. In this review paper CFS sections design, analysis, and use of artificial neural network are discussed.

This paper reports the numerical investigation carried out on the Cold-formed steel (CFS) built up columns strengthened with a Carbon fiber-reinforced polymer (CFRP) by using Finite element software ABAQUS/CAE. The CFS sections used in the investigation are built-up Cold formed box sections connected together by screws. Totally 24 columns are considered for the analysis in that twelve columns used are plain CFS and other 12 columns used are CFRP strengthened CFS columns. The geometric properties of the materials considered as 0.6 mm, 0.75 mm and 1 mm thickness with 300 mm, 500 mm and 700 mm depth respectively. All the built-up columns were modeled and analysed using ABAQUS software. From the analysis results, the Ultimate load capacity, buckling behavior and Load-lateral displacement of the Plain and CFRP strengthened columns are obtained and presented in this paper. It is also noted that the numerical results obtained from the ABAQUS software is in good agreement with the experimental results.

This study presents the prediction of the ultimate load carrying capacity of cold formed steel (CFS) built-up back-to-back channel columns having fixed boundary conditions under axial compressive load. There were 60 non-linear finite element models developed in ABAQUS, 12 of which were validated using experimental data while the remaining 48 models were validated based on AISI specification design standards. The finite element analysis and experimental results were also compared to the ultimate strength from the AISI specification. A parametric study was carried out using the validated finite element model in addition to the use of machine learning models to predict the ultimate load of CFS sections. Here, the machine learning models such as Artificial Neural Network (ANN), Gradient Tree Boosting (GTB) and Multivariate Adaptive Regression Splines (MARS) were developed for comparative evaluation of model predictions. Based on the performance evaluation using several statistical indices, MARS and GTB models were found to provide relatively accurate predictions of the ultimate load of CFS sections.

This paper presents the investigation on built-up cold-formed steel column sections strengthened with carbon fibre-reinforced polymer (CFRP). Here, box-shaped column sections were fabricated by screwing together with individual channels. Behaviour of twelve plain and twelve CFRP-strengthened box sections was studied under axial compression. Three lengths of 300 mm, 500 mm and 700 mm with section thickness of 0.6 mm, 0.75 mm and 1 mm were studied in the investigation. The cold-formed steel design procedure suggested by North American specification AISI S100-16 and Euro code 3 (EC3) provisions was applied and compared with the experimental results, which were in good agreement. The results also indicate that the proposed strengthening technique is effective in repair and increasing the load-carrying capacity of cold-formed steel built-up box column.

Progressive collapse is the phenomena in which the local failure of a primary structural member results in partial or total structural system damage, without any proportionality between the initial and final failure. In the present study, multi-story structure is considered. The modeling and analysis are carried out using SAP2000 software. The different bracing systems such as X, V, K and diagonal bracing are used to analysis the structural behavior. The structure is verified for column removal at 3 different locations such as corner, center and interior. It is observed that maximum vertical deflection is found in interior column removal case and least in corner column removal. Interior column case was observed to be most critical case in progressive collapse. There is a raise in axial load and the load distribution to the adjacent columns for column removal at location 1, 2, 3 is around 30%, 25%, and 20%, respectively. The X bracing system performs well in case of progressive collapse.

This paper presents experimental findings of carbon fibre reinforced polymer (CFRP) strengthened cold-formed steel built-up channels subjected to axial load. In order to sensitively detect the buckling strengths and failure modes, twenty-four built-up columns were tested in which twelve columns were unadorned and another twelve columns were strengthened using CFRP sheets. The columns were tested for ultimate load, lateral displacement and failure modes. Tension coupon tests were conducted to determine the material properties of test specimens. The experimental results were compared with the design load calculated according to AISI S100-16 specification and are found to be in good agreement. It is established that the use of CFRP for strengthening increases the ultimate load and delays the buckling.

This paper present the experimental results obtained from the incorporation of ceramic waste tile (CWT) and quarry dust (QD) as a partial replacement to coarse and fine aggregate with different percentages in concrete.The concrete specimens were casted wi ceramic waste tile as coarse aggregate replacing with natural coarse aggregate at 0%, 5%, 10%, and 15%, followed by quarry dust replacing with fine aggregate at 0%, 20%, 40%, and 60% respectively.The experimental results at fresh state shows that the maximum slump is attained at 0% which is 50mm and the compacting factor test result shows 0.92mm at 0%.A total 54 samples of Cubes, cylinders and beams were cast and tested for compressive strength, split tensile strength and flexural strength at 7,14 and days of curing respectively.The results show that the percentage increase in CWT and QD will decrease in strength compared to the normal concrete.The experimental compressive strength, flexural strength and tensile strength concrete containing ceramic tile and quarry dust are presented in this paper.

Objectives: Structural steel can be broadly classified into hot rolled steel and cold formed steel (CFS). CFS manufactured from roll forming or press braking operation. The main advantage of CFS sections are thin wall and light weight. These thin wall leads to buckling of structural member. This paper aims to study the capacity of Carbon Fibre Reinforced Polymer (CFRP) strengthened CFS channel beams, buckling behaviour, deflection and cost studies on CFRP strengthening. Methods/Statistical analysis: In this study three different length of CFS channel sections are selected for testing in which beams are tested for bending. All the beams were tested in plain section and CFRP strengthened series in universal testing machine. Findings: Experimental results shows the enhancement in capacity of beam due to CFRP strengthening. The local and distortional buckling modes were observed as failure modes. Experimental results were compared with maximum load resistance calculated by AISI specification which are in good agreement. Cost studies were compared on replacing a new member with CFRP strengthened channel member. Improvements/Applications: CFRP strengthening is suitable for enhancement of capacity of beams and repair. This CFRP strengthening technique can applied to reinforced concrete members, hot rolled steel, in filled steel tubes and bridges etc. Keywords: Beam, CFRP, CFS, Channel, Cost, Repair

This paper presents the experimental investigation conducted on cold formed steel built up box shaped beams strengthened using CFRP. Box shaped cross sections were made of connecting two channel members using screws. The selected beam specimens were 300mm, 400mm and 500mm in length with screw spacing of 100mm and edge distance of 50mm. Firstly, Single point load bending test were carried out to determine the maximum load, deflection and failure modes for plain sections. Secondly, same bending test were repeated for CFRP wrapped sections to determine the maximum load, deflection and failure mode. The maximum load capacities calculated using AISI specifications are compared with experimental maximum loads which are in good agreement. (c) 2017 The Authors. Published by IASE.

Cold formed steel differ from hot rolled steel by its lesser thickness and weight. The cold formed steel applicable in roof purlin, pipe racks and wall panels etc. Due its lesser wall thickness the cold formed steel member subjected to buckling. The enhancement of load carrying capacity of the cold formed steel member can be achieved by external strengthening of CFRP. In this study cold formed channel members connected back to back to form I shaped cross section using screws. These built up beam members were 300mm, 400mm and 500mm in length with 100mm screw spacing and edge distance of 50mm were chosen for testing. CFRP fabric cut according to length, width of built up beams and wrapped outer surface of beam using epoxy resin. Experiments were carried out in two sets firstly plain built up beams and secondly CFRP wrapped beams. The test results shows that increased load carrying capacity and reduction in deflection due to CFRP strengthening. Experimental results were compared with AISI standards which are in good agreement. Experimental results shows that CFRP strengthening is economic and reliable.

This paper discuss the ultimate strength and design of cold formed built up steel sections by experimental and American (AISI-2007) standard method. For the experimental studies two cold formed steel channel members were connected back to back to form built up I sections and these channels are attached with screws. There were two sets of specimens were prepared such as plain and carbon fibre reinforced polymer (CFRP) strengthened built up columns. The prepared specimens tested for axial compression and results were noted. A series of parametric studies were also carried out by two different thickness and column length. The test results were compared with American (AISI-2007) Standards and proposed design equation using modular ratio concept method. The details of these investigation and the outcomes are presented in this paper.

The incorporation of silica fume into the normal concrete in the present days to produce the tailor-made high strength concrete the strength of concrete increases with the incorporation of silica fume in partial replacement of cement in a high strength concrete. The main objective of this research work has been made to investigate the compressive strength and flexural strength of concrete by incorporating silica fume. In this present research work 5 (five) different mix of concrete were made incorporating silica fume. These experiments were carried out by replacing cement with different percentages of silica fume at a single constant water-cement ratio keeping other mix design variables constant. The silica fume was replaced by 0%, 5%, 10% and 15% with constant water-cement ratio of 0.40. The silica fume incorporated concrete was tested for 1 day, 7 days, 28 days and 60 days compressive strength and flexural strength. Other fresh concrete properties like compacting factor and slump were also determined for five mixes of concrete to find the workability of the concrete.

This paper represents the experimental results of a concrete using seashell as a partial replacement. The present work is to investigate the effects of seashells in concrete production to produce high strength concrete. The compressive strength, flexural strength and split tensile strength tests were carried out with different proportioned sea shells at different curing days, as well as finding the optimum percentages of sea shell replacements to give targeted strengths. The concrete samples were prepared by adding seashells about 0%, 10%, 20%, 30% and 40% as a partial replacement to coarse aggregate. All these samples were cured for 7 days, 14 days and 28 days before the compressive, split tensile and flexural test were carried out. A total of 135 specimens were casted and tested for five mixtures in their proportions so as to determine the mechanical properties of the concrete. It should be noted that no additives were added to the mix except sea shell as a partial replacement coarse aggregate. A total of 45 cubes were casted for compressive strength test with dimensions of (150mm x150mm x150mm) and 45 prisms were casted for flexural strength test with dimensions of (100mm x100mm x300mm) and remaining 45 cylinders casted for split tensile strength test with dimensions of (100mm x200). After these tests were carried out, the results were used to compare with those of the control experiment. The results showed a decrease in density due to increase in seashell content and a high strength value obtained for 20% replacement. It was noted that implementing seashells in the concrete mix can be used to produce a lightweight concrete with high strength.