Journal of Materials and Engineering Structures
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Prediction and Feature Analysis of Self-Compacting Concrete Strength Using Machine Learning with Recycled Coarse Aggregate and Supplementary Cementitious Materials
Full text linkThis study presents a machine learning based approach to predict the CS of SCC incorporating RCA and supplementary cementitious materials (SCMs), aimed at promoting sustainable construction practices. A dataset comprising 337 SCC mixes with varying input parameters such as cement (40–635 kg/m³), SCMs (0–592 kg/m³), water to binder ratio (0.25–0.45), and curing age (7–120 days) was utilized. Five regression models were evaluated- Extra Trees, AdaBoost, SVR Gradient Boosting, and ANN. Among them, Gradient Boosting achieved the highest accuracy with R² = 0.8876, RMSE = 4.032 MPa, and MAE=2.6382 MPa. Extra Trees followed closely with R²=0.8309 and RMSE=4.9444 MPa. SHAP interpretability analysis revealed that cement content and curing age were the most influential parameters in predicting CS. The study confirms the effectiveness of ML models in replacing time consuming lab tests and provides an interpretable, data driven framework for optimizing concrete mix design using industrial waste materials
Study on Pipe Strut Dimensions Optimization for Strutted Box Girder Bridge Using FEM
Full text linkAlthough the concept of a concrete box girder bridge with strutted wing slabs was proposed long ago as a means to expand the width of bridge sections without significantly increasing the self-weight of the superstructure, research on this structural system remains limited. Using data from a full-scale box girder bridge experiment conducted as part of the Ring Road II Viaduct Project in Vietnam, a finite element model was developed to investigate the influence of the steel struts' geometrical dimensions on the structural behavior of box girders. This paper examines key structural responses, including deflection, stress in the concrete slab, and stress in the steel struts, under varying steel strut dimensions—namely, thickness and diameter. The findings reveal that the diameter and thickness of the steel struts significantly affect beam deflection and strut stress, while their impact on slab stress is negligible. Furthermore, the paper provides practical recommendations for selecting optimal steel strut dimensions and highlights future research directions to enhance the design of such structures. These insights aim to benefit both practicing engineers and researchers in the field
A Multi-Layer Perceptron (MLP) Neural Network Approach for Predicting Pressuremeter (PMT) Test Results in Geotechnical Engineering
Full text linkThis study proposes a hybrid machine learning approach using a Multi-layer Perceptron Neural Network (MLP) to predict soil pressuremeter parameters, leveraging a database of 120 soil samples from the Algeria railway line project. The MLP model demonstrated superior performance, achieving a high coefficient of determination (R² 0.93) and low RMSE, indicating strong agreement between predicted and experimental values. Comparative analysis with multiple regression highlighted the MLP’s efficiency in prediction speed and accuracy, though its "black-box" nature limits interpretability of individual parameter influences. While regression provided explicit equations for variable effects, the study underscores the importance of expanding the database with representative inputs to further enhance predictive reliability. The results support MLP as a robust tool for geotechnical forecasting, balancing precision with computational efficiency
Experimental Evaluation for Improving the Engineering Properties of Concrete Using Partial Replacement of Natural Waste
Full text linkWith the global growth of building construction and infrastructure around the world, the global demand for concrete materials in the construction industry is increasing day by day. Natural waste can be considered an alternative substitute that can be partially mixed into concrete up to a certain limit while maintaining the strength and properties of the concrete for a long duration. In this research, crushed coconut shell as fine and coarse aggregate and coconut shell ash as cement are used as a partial replacement for fine aggregate, coarse aggregate, and cement in concrete. A mix design of M20-grade concrete was prepared as per BIS 1026 2019, and 150 × 150 × 150 mm³ sized cubes of concrete were designed. Concrete materials were replaced with a different form of coconut shells, as cement was replaced with coconut shell ash. In this research, the effects of replacing cement, fine aggregate (FA), and coarse aggregate (CA) were tested in concrete with alternative materials (CSA, CSFA, CSCA) at different levels (0–25%). Concrete strength was measured after 28 days, which represents reduced strength from 24.58 MPa (0%) to 12.80 MPa (25%) in replacing cement with CSA. Increased strength at 5% (28.58 MPa) but dropped to 8.29 MPa at 25% in replacing FA with CSFA. Reduced strength to 8.14 MPa at 25% in replacing CA with CSCA. Replacing all three together lowered strength to 7.25 MPa at 25%. So, only replacements of small proportion (CSFA at 5%) can improve strength, but higher percentages reduce concrete performance. After being prepared, the mould was kept for curing for 3, 7, 14, and 28 days, after which its compressive strength was tested. The result analysis shows that mixing up to 5-10% of crushed coconut shell (CCS) and coconut shell ash (CSA) in concrete does not affect the strength or durability. The limitation of using this natural waste is that it can be substituted up to a certain limit, and the strength graph decreases significantly beyond that limit
Innovations in Green Construction: A Comprehensive Review of Limestone Calcined Clay Cement
Full text linkCement production is a major environmental concern, releasing nearly one tonne of carbon dioxide for every tonne of cement produced making it one of the largest industrial contributors to global CO₂ emissions. Limestone Calcined Clay Cement concrete is a new type of concrete that uses a mix of limestone and calcined clay as its binder. Combining limestone and calcined clay creates a low-clinker cement blend with impressive early-age strength. The durability of these cement systems is closely linked to their pore structure, which directly impacts how easily substances can move through the material. It is an eco-friendly, cost-effective, and high-performing option, offering a sustainable alternative to traditional Portland cement. This review paper provides insights into the edge-cutting features of LC3, manufacturing process of LC3. This review paper also details the emphasis on composition, properties, and applications of LC3. The review paper summarizes the findings from the numerous literature surveys, research gaps, and further areas for research and development
A review on carbonation in cement composites and its impacts on durability and potential for CO₂ sequestration
Full text linkCarbonation is a key process affecting the durability, strength, and environmental impact of concrete. While it refines pore structure and enhances strength, it also lowers alkalinity, leading to reinforcement corrosion risks. The carbonation rate is influenced by CO₂ concentration, relative humidity, w/c ratio, binder composition, and exposure conditions, all of which determine its impact on concrete performance. This review examines the mechanism of carbonation, influencing factors, effects on mechanical and durability properties, and the relationship between natural and accelerated carbonation. The study highlights that carbonation influences mechanical and durability properties, with research showing that moderate carbonation curing increases compressive strength by 10–30%. Still, excessive carbonation leads to C-S-H decalcification, reducing durability. While accelerated carbonation tests show a carbonation rate 10-15 times faster than natural exposure, the corresponding increase in carbonation depth is only 2.5-5 times, indicating a non-linear relationship that complicates the direct prediction of long-term field performance from laboratory data. Case studies confirm that protected concrete surfaces exhibit minimal carbonation depth (10 mm in 100 years), whereas exposed surfaces reach up to 50 mm, causing surface deterioration. Additionally, carbonation contributes to CO₂ sequestration, with Itaipu Dam alone absorbing 13,384 tons of CO₂ over 35 years. This comprehensive analysis emphasizes the need for refined, experimentally validated models to accurately predict carbonation and harness its benefits while mitigating risks in future sustainable concrete design
Comparative Study on Dynamic Behaviour of Thick Beams on Foundation: Winkler, Pasternak and Kerr Foundation
Full text linkA comparative study amongst different types of elastic foundation of a bi-directionally graded beam mounted on elastic foundation is carried out. A single parameter Winkler, two parameters Pasternak and three parameters Kerr foundation are considered as elastic foundation. Bi-directional variation of the beam material is considered along the both thickness and length directions following power law distribution function. The kinematics of the beam is defined following Timoshenko beam theory. To obtain the geometric nonlinear free vibration response the problem is solved in two stages. At initial stage the static part of the problem is solved under loaded condition. Small amplitude dynamic analysis is followed next on the pre-deflected beam to get large amplitude free vibrational response. Displacement based Rayleigh-Ritz principle is utilized to formulate the problem and employing a numerical method the problem is solved. New sets of results are prepared for various combinations of system parameters to investigate the effect of various types of elastic foundations on dynamic behaviour
A Comparative Analysis of Isolated and Tapered Footing with Insertions for Eccentric Load
Full text linkConventional footings for boundary line columns often suffer from tilt and uneven pressure distribution due to eccentric loading, reducing their performance. While combined and strap footings are commonly used to address eccentric loads, they rely on adjacent footings, limiting their effectiveness in independent foundation systems. This study develops and evaluates two innovative designs—Isolated Footing with Insertion (IFI) and Tapered Footing with Insertion (TFI)—to mitigate these challenges. Using finite element analysis in ANSYS, the performance of these designs was assessed under varying eccentric loading conditions, with insertion depth ratios (H/B) ranging from 0 to 1.0 and eccentricity ratios (x/B) from 0.1 to 0.5. Results indicate that both designs significantly outperform conventional footings, with the IFI design reducing settlement and tilt by up to 5 times at H/B = 1.0. The TFI design demonstrated further improvements, delivering 1.18 to 1.39 times better performance than IFI across all insertion depths while reducing material usage by 15–20%. Optimal performance for TFI was observed at H/B = 0.4, striking a balance between structural efficiency and material economy. These findings highlight TFI as an efficient and economical solution for managing eccentric loads, particularly for boundary columns and space-constrained sites
Effects of asphalt binder and aggregate gradation on dynamic modulus, resilient modulus and moisture resistance of asphalt concretes
Full text linkIn this paper, the effects of asphalt binder and aggregate gradation on dynamic modulus (|E*|), resilient modulus (Mr) and moisture resistance of asphalt concretes (AC) have been studied. Six different asphalt concretes are designed with two aggregate gradations (nominal maximum aggregate size of 12.5 and 19 mm) and three types of asphalt binders (penetration grades of 40/50, 60/70 and a polymer-modified bitumen PMB3). Dynamic modulus, resilient modulus and Tensile Strength Ratio (TSR) test have been performed on the studied AC. The |E*| values obtained from dynamic modulus test at various frequencies and temperatures are simulated using a linear viscoelastic model 2S2P1D. Experimental results indicate the clear effect of asphalt binder and aggregate gradation on mechanical properties of tested AC. The 2S2P1D model successfully simulates the |E*| master curve with high precision (R² values from 0.84 to 0.97). The roles of 40/50 and PMB3 asphalt binder in enhancing the performance of asphalt concretes are also clearly demonstrated under each specific temperature condition and mechanical property type
Prediction of Compressive Strength by Considering Practical Consideration Non-destructive Test by Artificial Neural Network
Full text linkAccurate assessment of concrete compressive strength is critical for evaluating structural performance. While nondestructive testing (NDT) methods, such as Schmidt rebound hammer tests, offer rapid and NDT gives result with reasonable accurate based on environmental factors such as temperature, humidity etc of site and condition in which test is performed. Destructive testing (DT) methods, like core cutting, provide direct and accurate results. This study aimed to bridge the gap between these approaches by developing predictive models that correlate DT and NDT results. Experimental work involved 126 laboratory-prepared samples (grades M10–M40) with curing age of 14 day and 28 day and 231 field samples from a 20-year-old structure, tested using both methods. Total 357 no. of data samples were created with different mix proportion of design, curing ages and on-site environmental exposed concrete structure without unknown grade. Most of the researches were done while preparation of samples in the laboratory. For these purposes of taking mixing both variations such as control (Laboratory) and uncontrolled(on-site) samples were to prepare as a practical condition for prediction. For generation of predict model 70% data was used with methods such as regression analysis and Cascade forward back propagation neural network (CFBPNN) were used for investigation. To validate the prediction 30% data was used which was not used in model generation. The prediction results show that the coefficients of determination (R2) of the Regression analysis and the CFBPNN prediction models for the test set of concrete compressive strength are 95% and 99% respectively ANN model founded to be more accurate as compare to regression analysis. The validation by R2 of the Regression analysis and the CFBPNN prediction model for the compressive strength for above dataset was 89.0% and 98%. Statistical metrics (MSE, RMSE, MAPE) further confirmed the neural network’s superior accuracy