Investigation of the effect of silica fume and synthetic foam additive on cell structure in ultra-low density foam concrete

In this study, the properties of ultra-low density foam concrete with silica fume substitute and synthetic foam additive were investigated. Two different references and silica fume substituted foam concretes with densities of 220 and 200 kg/m(3) were produced. Silica fume was used as the replacement material and its ratio in the mixtures was kept constant at 5% by weight. According to the results of the study, the compressive strengths and the thermal conductivity coefficients of the references and silica fume substituted foam concretes with densities of 220 and 200 kg/m(3) were found to be 0.26, 0.21 and 0.32, 0.26 Mpa at 28 days and 0.073, 0.069 and 0.068, 0.060 W/mK, respectively. In addition, the behavior of foam concrete at high temperatures was investigated using a flame source, which can reach up to 1200 degrees C, since temperatures usually exceed 1000 degrees C during a fire. At the end of ten minutes, the heat permeability of silica fume substituted foam concrete exposed to a 1200 degrees C temperature was 6.5% and 5.3%, which was better than reference foam concretes, respectively. As a result, silica fume has positively affected the compressive strength at later ages and thermal conductivity properties of foam concrete

KARAKTERISTIK BETON RINGAN MENGGUNAKAN FOAM AGENT (Sodium Lauryl Sulfate) SEBAGAI BUSA

Implementasi beton sebagai bahan konstruksi merupakan elemen dominan yang digunakan dan dikembangkan dengan berbagai penelitian yang dirancang untuk mendapatkan beton yang memiliki ketahanan terhadap perubahan konfigurasi akibat gaya kerja dan pengaruh lingkungan, beton ringan umumnya memiliki berat jenis di bawah 2,0 N/mm2, penerapan foam agent dalam mortar menjadi beton ringan, bertujuan untuk mengurangi berat beton dan memiliki massa yang rendah dengan menggunakan bahan berupa sodium lauryl sulfate yang telah diekspansi menjadi busa pada campuran mortar apa adanya. Diketahui bahwa penerapan material konstruksi bermassa rendah akan mengurangi berat struktur yang tentunya akan mempengaruhi konstruksi secara keseluruhan. Sehingga potensi penggunaan material konstruksi bangunan yang ringan dan ramah lingkungan merupakan salah satu upaya untuk mendukung konstruksi dan mendukung program pemerintah dalam meminimalisir eksploitasi material alam. Campuran beton dan karakteristik kuat tekan beton silinder 100 mm x 200 mm dengan kuat tekan rata-rata 5,702 MPa, kuat tarik diperoleh 0,522 MPa, modulus elastisitas 3,823 MPa dan kuat lentur balok 1,232 MPa. Penelitian eksperimental untuk mengetahui karakteristik kekuatan beton ringan berbahan busa mengacu pada Standar Nasional Indonesi

The determination of ground granulated concrete compressive strength based machine learning models

The advancement of machine learning (ML) models has received remarkable attention by several science and engineering applications. Within the material engineering, ML models are usually utilized for building an expert system for supporting material design and attaining an optimal formulation material sustainability and maintenance. The current study is conducted on the based of the utilization of ML models for modeling compressive strength (Cs) of ground granulated blast furnace slag concrete (GGBFSC). Random Forest (RF) model is developed for this purpose. The predictive model is constructed based on multiple correlated properties for the concrete material including coarse aggregate (CA), curing temperature (T), GGBFSC to total binder ratio (GGBFSC/B), water to binder ratio (w/b), water content (W), fine aggregate (FA), superplasticizer (SP). A total of 268 experimental dataset are gather form the open-source previous published researches, are used to build the predictive model. For the verification purpose, a predominant ML model called support vector machine (SVM) is developed. The efficiency of the proposed predictive and the benchmark models is evaluated using statistical formulations and graphical presentation. Based on the attained prediction accuracy, RF model demonstrated an excellent performance for predicting the Cs using limited input parameters. Overall, the proposed methodology showed an exceptional predictive model that can be utilized for modeling compressive strength of GGBFSC

Comparative analysis of gradient-boosting ensembles for estimation of compressive strength of quaternary blend concrete

Concrete compressive strength is usually determined 28 days after casting via crushing of samples. However, the design strength may not be achieved after this time-consuming and tedious process. While the use of machine learning (ML) and other computational intelligence methods have become increasingly common in recent years, findings from pertinent literatures show that the gradient-boosting ensemble models mostly outperform comparative methods while also allowing interpretable model. Contrary to comparison with other model types that has dominated existing studies, this study centres on a comprehensive comparative analysis of the performance of four widely used gradient-boosting ensemble implementations [namely, gradient-boosting regressor, light gradient-boosting model (LightGBM), extreme gradient boosting (XGBoost), and CatBoost] for estimation of the compressive strength of quaternary blend concrete. Given components of cement, Blast Furnace Slag (GGBS), Fly Ash, water, superplasticizer, coarse aggregate, and fine aggregate in addition to the age of each concrete mixture as input features, the performance of each model based on R2, RMSE, MAPE and MAE across varying training–test ratios generally show a decreasing trend in model performance as test partition increases. Overall, the test results showed that CatBoost outperformed the other models with R2, RMSE, MAE and MAPE values of 0.9838, 2.0709, 1.5966 and 0.0629, respectively, with further statistical analysis showing the significance of these results. Although the age of each concrete mixture was found to be the most important input feature for all four boosting models, sensitivity analysis of each model shows that the compressive strength of the mixtures does increase significantly after 100 days. Finally, a comparison of the performance with results from different ML-based methods in pertinent literature further shows the superiority of CatBoost over reported the methods

Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete

The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various soft-computing models and existing empirical equations. Sensitivity analysis has also been conducted to identify the most influential parameters to the SFRC shear strength

Increasing Sustainability in Buildings Through Energy-Efficient Concrete

The energy performance of buildings is influenced by a wide range of climatic and design-related variables, including but not limited to ambient temperature, heating and cooling systems, and thermal properties of building elements. This thesis explored the magnitude of the impact of the thermal properties of concrete compared to other influential factors and assessed their critical role in the energy performance of buildings. To this end, several approaches have been employed to improve the thermal performance of concrete, such as partial to full replacement of cement and natural aggregates with supplementary cementitious materials and recycled concrete aggregates, respectively, resulting in the production of lightweight concrete. However, incorporating recycled contents into concrete mixes beyond certain percentages can negatively impact the mechanical performance of concrete, which poses a challenge for engineers and designers balancing thermal, environmental, and mechanical performances. With the goal of spanning the mentioned requirements, this thesis proposed an AI-assisted framework integrating data-driven modelling techniques and multi-objective optimisation algorithms to optimise recycled aggregate concrete mixes targeting energy performance-related and economic objectives without compromising their mechanical strength. In this sense, incorporating recycled contents and air bubbles into concrete mixes was found to be an effective approach to address some hurdles associated with concrete 3D printing, which is a promising technique for large-scale construction projects due to its speed and cost-efficiency. The results showed that increasing air voids allowed for replacing recycled content beyond commonly used percentages, resulting in lightweight and ultra-lightweight 3D printable cementitious composites with significant thermal conductivity improvements

An Intelligent Approach for Predicting Mechanical Properties of High-Volume Fly Ash (HVFA) Concrete

Plastic waste (PW) is a major soild waste, which its generation continues to increase globally year in and year out. Proper management of the PW is still a challenge due to its non-biodegradable nature. One of the most convenient ways of managing plastic waste is by using it in concrete as a partial substitute for natural aggregate. However, the main shortcomings of adding plastic waste to concrete are a reduction in strength and durability. Hence, to reduce the undesirable impact of the PW in concrete, highly reactive additives are normally added. In this research, 240 experimental datasets were used to train an artificial neural network (ANN) model using Levenberg Marquadt algorithms for the prediction of the mechanical properties and durability of high-volume fly ash (HVFA) concrete containing fly ash and PW as partial substitutes for cement and coarse aggregate, respectively, and graphene nanoplatlets (GNP) as additives to cementitious materials. The optimized model structure has five input parameters, 17 hidden neurons, and one output layer for each of the physical parameters. The results were analyzed graphically and statistically. The obtained results revealed that the generated network model can forecast with deviations less than 0.48%. The efficiency of the ANN model in predicting concrete properties was compared with that of the SVR (support vector regression) and SWLR (stepwise regression) models. The ANN outperformed SVR and SWLR for all the models by up to 6% and 74% for SVR and SWLR, respectively, in the confirmation stage. The graphical analysis of the results further demonstrates the higher prediction ability of the ANN. Doi: 10.28991/CEJ-2023-09-09-04 Full Text: PD

Parametric Assessment of Concrete Constituent Materials Using Machine Learning Techniques

Nowadays, technology has advanced, particularly in machine learning which is vital for minimizing the amount of human work required. Using machine learning approaches to estimate concrete properties has unquestionably triggered the interest of many researchers across the globe. Currently, an assessment method is widely adopted to calculate the impact of each input parameter on the output of a machine learning model. This paper evaluates the capability of various machine learning methodologies in conducting parametric assessments to understand the influence of each concrete constituent material on its compressive strength. It is accomplished by conducting a partial dependence analysis to quantify the effect of input features on the prediction results. As a part of the study, the effects of machine learning method selection for such analysis are also investigated by employing a concrete compressive strength algorithm developed using a decision tree, random forest, adaptive boosting, stochastic gradient boosting, and extreme gradient boosting. Additionally, the significance of the input features to the accuracy of the constructed estimation models is ranked through drop-out loss and MSE reduction. This investigation shows that the machine learning techniques could accurately predict the concrete's compressive strength with very high performance. Further, most analyzed algorithms yielded similar estimations regarding the strength of concrete constituent materials. In general, the study's results have shown that the drop-out loss and MSE reduction outputs were misleading, whereas the partial dependence plots provide a clear idea about the influence of the value of each feature on the prediction outcomes

Flexural behavior of one-way ferrocement slabs with fibrous cementitious matrices

Concrete compressive strength enhancement is considered as one of the popular topics in the field of civil engineering. It has received a massive attention by material and structural engineers over the past decades. The aim of this study is to investigate thin mortar matrix for the impacts of the combination of reinforcing steel meshes with discontinuous fibers, and to do this, one-way Ferrocement slabs were tested under bending with steel fibers and meshes, focusing more on the number of mesh layers (1, 2, & 3) as the studied parameter. The percentages of fiber content as volumetric ratio 0.25, 0.5 and 0.75 and type of fibers golden steel fibers and waste aluminum fibers from waste metallic cans. Results showed that at general the adding of fibers regardless of its type increased the ductility of tested slabs. In addition, results showed that steel fibers are more effective than aluminum fibers