Modified Artificial Neural Networks and Support Vector Regression to Predict Lateral Pressure Exerted by Fresh Concrete on Formwork

In this study, a modifed Artifcial Neural Network (ANN) and Support Vector Regression (SVR) with three diferent optimization algorithms (Genetic, Salp Swarm and Grasshopper) were used to establish an accurate and easy-to-use module to predict the lateral pressure exerted by fresh concrete on formwork based on three main inputs, namely mix proportions (cement content, w/c, coarse aggregates, fne aggregates and admixture agent), casting rate, and height of specimens. The data have been obtained from 30 previously piloted experimental studies (resulted 113 samples). Achieved results for the model including all the input data provide the most excellent prediction of the exerted lateral pressure. Additionally, having diferent magnitudes of powder volume, aggregate volume and fuid content in the mix exposes diferent rising and descending in the lateral pressure outcomes. The results indicate that each model has its own advantages and disadvantages; however, the root mean square error values of the SVR models are lower than that of the ANN model. Additionally, the proposed models have been validated and all of them can accurately predict the lateral pressure of fresh concrete on the panel of the formwork

Utilization of Bitumen Modified with Pet Bottles as an Alternative Binder for the Production of Paving Blocks

This study considers the utilization of bitumen modified with molten polyethylene terephthalate (PET) waste bottles as an alternative binder in paving blocks. PET waste was used at 2, 4, 6, 8, and 10% to modify bitumen in the production of paving blocks. Compressive strength test and skid resistance test were conducted on the paving block samples to evaluate their mechanical strength properties, while water absorption and the Cantabro abrasion tests were carried out to ascertain the durability of the paving block samples. The PET-modified bitumen paving blocks (PMBPB) have enhanced compressive strength and skid resistance compared to unmodified bitumen paving blocks. Also, a significant reduction in water absorption rate of up to 56% was achieved in PET-modified bitumen paving blocks (PMBPB) compared to the unmodified sample. The abrasion loss in the PMBCB samples was the least compared to that in normal cement paving blocks and unmodified bitumen paving blocks. The maximum compressive strength and least water absorption for the PET-modified bitumen concrete paving blocks were obtained at a 10% PET replacement level. It can be concluded that enhanced compressive strength and durability in cement paving blocks and unmodified bitumen paving blocks could be achieved with the use of PET modified bitumen in concrete paving block production, and this will also encourage PET waste recycling and contribute meaningfully to sustainability in concrete paving block production. Doi: 10.28991/CEJ-2023-09-01-08 Full Text: PD

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

The effect of various parameters on the flexural strength (FS) of ultra-high-performance concrete (UHPC) is an intricate mechanism due to the involvement of several inter-dependent raw ingredients. In this digital era, novel artificial intelligence (AI) approaches, especially machine learning (ML) techniques, are gaining popularity for predicting the properties of concrete composites due to their better precision than typical regression models. In addition, the developed ML models in the literature for FS of UHPC are minimal, with limited input parameters. Hence, this research aims to predict the FS of UHPC considering extensive input parameters (21) and evaluate each their effect on its strength by applying advanced ML approaches. Consequently, this paper involves the application of ML approaches, i.e., Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Gradient Boosting (GB), to predict the FS of UHPC. The GB approach is more effective in predicting the FS of UHPC precisely than the SVM and MLP algorithms, as evident from the outcomes of the current study. The ensembled GB model determination coefficient (R2) is 0.91, higher than individual SVM with 0.75 and individual MLP with 0.71. Moreover, the precision of applied models is validated by employing the k-fold cross-validation technique. The validity of algorithms is ensured by statistical means, i.e., mean absolute error and root mean square errors. The exploration of input parameters (raw materials) impact on FS of UHPC is also made with the help of SHAP analysis. It is revealed from the SHAP analysis that the steel fiber content feature has the highest influence on the FS of UHPC

Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector

Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector.Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought.Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model.Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis

Thermal conductivity, microstructure and hardened characteristics of foamed concrete composite reinforced with raffia fiber

Researchers have become enthralled with using natural fiber, which is a waste product from industrial processes, as an additive in cement-based materials. This is due to the fact that natural fiber is inexpensive, has principal carbon neutrality, and is obtainable in large quantities. Additionally, this fiber is made from a renewable resource. Hence it has a low density and is amenable to undergoing chemical alteration. The idea of this investigation is to discover the reactivity of raffia (raphia vinifera) fiber (RF) in low-density foamed concrete (FC). FC density of 950 kg/m3 was utilized. Workability, density, thermal conductivity, SEM analysis, compressive, bending, and tensile strengths were the parameters that were quantified and assessed. Based on the outcomes, it has been determined that the mechanical properties and thermal conductivity of FC-RF composites may be enhanced by using RF with an ideal reinforcing fraction content of 6%. Slump flow gradually decreased from 2%to8%RFfraction content. The lowest slump flow was achieved by adding RF to the FC mixture at a fraction content of 8%. The density of FC-RF composites shows a developing tendency, likely because of the RF's comparatively high specific gravity and increasing fraction content. The addition of RF to FC considerably enhances the material's compressive, bending, and tensile strength. The optimal strength characteristics emerged when 6% RF was added to FC. Besides, the FC thermal conductivity improves as the weight percent of RF increases because the porous structure of FC with RF allows it to absorb heat

BEHAVIOR and strengthening of rc t- girders in torsion and shear

Object Tools Home Edit Reviev Forms Protec Help Forma AlignFind and Header &Footer ExtractBates Number Edit OCR Add Insert Bookmark Link Select ImageReplace A Watermark Tools Text /Amages Pages Page Marks Navigation Ahmed Daif PhD 2007 X ABSTRACT Failure of a structural clement under shear and torsion is brittle in natuze and should be avoided as it compromises the ductile behaviour of the structure. Under vatious cases of loading and geometric configuration, concrete structural members are subjected to significant torsion accompanied by cither bending, or shear and bending. A limited number of studics were conducted to study torsion and shear behaviour of reinforced concrete (RC) girders. Most of these studies were focused on rectangular girders. Nonc were condueted to investigate the strengthening of RC T-girders under combined torsion and shear. An experimental study was condlucted to investigate the shear and torsion behaviour of RC T-girders. Thre T-girders were tested while subjceted to three different ue to shear ratios selected to cover a wide range of the lorsion and shear interactions In addiion, the Shear and Torsion provisions of the North American Design Codes were assessed using the experimental data. It was found that code predictions are more accurate at low torque to shear ratios. In most damage problems, strengthening using fibre reinforced polymers ( an effective and convenient solution. An experimental investization to explore the proposed and implemented. The eftectiveness of the proposed techniques was evaluated torsivually, steligtlhened rectungufur Re idern Was propesed and validated using strengtliening of RC T- girders was conducted.supervision by : ahmed gh

An overview of the research trends on fiber-reinforced shotcrete for construction applications

In this study, data mining, followed by the scientometric analysis of fiber-reinforced shotcrete (FRS), was carried out for knowledge mapping, co-citations, and co-occurrence. The information needed for the analysis was retrieved from the Scopus search engine. Important publishing sources, keyword analysis, writers with the most contribution in citations and publications, the most-cited articles, and the regions most actively engaged in FRS research were identified throughout the data review process. Moreover, the need for FRS, the major constraints associated with their usage, and their possible solutions were discussed. The analysis of the bibliographic data showed that research publications on FRS progressed inconsistently till 2015, and over the past 6 years (2016–2021), publication numbers increased steadily, which exhibited the interest of academics in fiber-reinforced materials. The analysis of keywords in the field showed that the most common FRS research keywords are shotcreting, shotcrete, steel fibers, FRS, and fiber-reinforced materials. Keyword analysis showed that FRS is typically used for tunnel rock support and lining. Based on the review of relevant literature, research gaps have been identified, and future research has been suggested

Sensitivity and robustness analysis of adaptive neuro-fuzzy inference system (ANFIS) for shear strength prediction of stud connectors in concrete

The shear strength of stud connectors is essential for designing steel-concrete structures, which is assessed only through a push-out test or available design codes. An alternative technique that eliminates the need to conduct the push-out test is soft computing (SC). The performance of any machine learning (ML) based prediction model depends on the sensitive parameters used in the model development. This paper performs a sensitivity analysis on the shear strength prediction of stud connectors embedded in concrete. A system identification (SI) was conducted using an adaptive neuro-fuzzy inference system (ANFIS) to find the most sensitive combinations of input variables. Six different models were developed based on the SI results. Three machine learning algorithms, including ANFIS, extreme learning machine (ELM), and artificial neural network (ANN), were used to estimate the shear strength of stud connectors in each developed model. The results show that the number of studs (n) is the most sensitive parameter in predicting shear strength. Irrespective of concrete compressive strength (fc), the combination of the stud diameter (ϕ), number of studs, and stud spacing (s) can predict the shear strength with the accuracy of ±8.67 kN. The robustness of the three AI algorithms was evaluated using the Monte Carlo Simulation method. The individual conditional expectation (ICE) was also presented to visualize the correlation between the target shear strength and the six predictors. The results of this study show that sensitivity analysis is an essential tool for any data-driven ML model for accurate prediction

Self-Compacting Concrete with Partially Substitution of Waste Marble: A Review

Abstract Self-compacting concrete (SCC) is also seen as unsustainable since it uses a lot of natural resources. Recent researchers have focused on lowering construction costs and partially replacing cement with industrial waste. It is possible to effectively use various industrial wastes in concrete as cement or aggregates. Among these wastes, waste marble (WM) is a useful choice, and researchers have been interested in using WM in concrete for a couple of years. However, to pinpoint the advantages and recent advancements of research on WM as an ingredient of SCC, a comprehensive study is necessary. Therefore, the purpose of this study is to do a compressive evaluation of WM as an SCC ingredient. The review includes a general introduction to SCC and WM, the filling and passing capability of SCC, strength properties of SCC, durability, and microstructure analysis of SCC. According to the findings, WM improved the concrete strength and durability of SCC by up to 20% substitution due to micro-filling and pozzolanic reaction. Finally, the review also identifies research gaps for future investigations