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Author: Lotfi, BahramScopus Q: Q1

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    Sustainable Stabilization of Expansive Soils Using Waste Marble Powder and Expanded Polystyrene Beads: Experimental Evaluation and Predictive Modelling
    (Elsevier, 2026) Akis, Ebru; Citak, Mete; Lotfi, Bahram
    Expansive soils exhibit considerable volume changes with moisture fluctuations leading to serious challenges for civil infrastructure, causing structural instability, pavement distortion, and foundation damage. While lime and cement remain widely used stabilizers, recent research has increasingly focused on waste-derived materials such as marble powder (MP) and expanded polystyrene beads (EPSb) as promising alternatives. These materials provide a practical approach to soil stabilization while contributing to the reuse of industrial by-products. In this study, the engineering behavior of high-plasticity clay was improved through the inclusion of MP and EPSb as additive materials. MP was added at 0%, 5%, 10%, 15%, and 20%, and EPSb at 0%, 0.3%, and 0.9% by dry weight of the high plasticity clay. Both additives were used alone and in combination. Laboratory tests, including Standard Proctor, free swell (FS), and unconfined compressive strength (UCS), were conducted. The results confirmed that the additives effectively reduced the liquid limit (LL) by 20.1% and the plasticity index (PI) by up to 22.4%. Results showed that EPSb effectively reduced FS and UCS, while MP decreased FS and increased UCS up to an optimal content. The most effective mixes achieved a maximum reduction of 54.7% in free swell (FS) (at 20% MP and 0.9% EPSb content) and a maximum increase of 13.1% in unconfined compressive strength (UCS) (at 5% MP content) compared to the untreated soil. The compaction tests further revealed a general decrease in optimum moisture content (OMC) and a slight increase in maximum dry density (MDD) with increasing MP content. Accordingly, the free swell (FS) and unconfined compressive strength (UCS) of the treated soils were predicted using multiple linear regression (MLR) and artificial neural network (ANN) models, developed from both the current experimental dataset and previously published studies. Input variables included untreated FS and UCS values, additive percentages, and one index property. The ANN model demonstrated superior predictive capability, achieving R2 values of 0.955 and 0.874 for FS and UCS, respectively, compared to 0.411 and 0.618 obtained with MLR. These results highlight the robustness of ANN in capturing nonlinear soil behavior and underscore its reliability and accuracy, particularly under limited data conditions.
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    Citation - WoS: 41
    Citation - Scopus: 42
    Enhancing Machining Efficiency of Ti-6al Through Multi-Axial Ultrasonic Vibration-Assisted Machining and Hybrid Nanofluid Minimum Quantity Lubrication
    (Elsevier Sci Ltd, 2024) Namlu, Ramazan Hakki; Lotfi, Bahram; Kilic, S. Engin
    Ti-6Al-4V offers a balance of good strength with lightweight properties. Yet, Ti-6Al-4V poses machining challenges, including low thermal conductivity, chemical adhesion to cutting tools, and chip removal difficulties. To improve machining efficiency, Ultrasonic Vibration-Assisted Machining (UVAM) has emerged as a promising approach. UVAM has demonstrated reduced tool wear, cutting forces, and improved surface quality compared to Conventional Machining (CM). Additionally, Minimum Quantity Lubrication (MQL) methods offer sustainable coolant alternatives, with recent research focusing on Nanofluid-MQL (NMQL) and Hybrid Nanofluid-MQL (HNMQL) for enhanced performance. Although there exists a body of literature showcasing the promising effects of UVAM and MQL methods individually, comprehensive investigations into the synergistic effects of these methodologies remain limited. This study addresses these critical research gaps by conducting a systematic examination of combined application of multi-axial UVAM and HNMQL. Specifically, it delves into the comparison of different vibration directions within UVAM, evaluates the effectiveness of UVAM when combined with cutting fluids incorporating Al2O3 and CuO nanoparticles in NMQLs and HNMQLs, and contrasts these novel approaches with conventional machining methods. The study unfolds in three distinct stages. The first stage examines the ultrasonic cutting mechanism and its combined application with the MQL technique. In the second stage, the study investigates the physical properties of the cutting fluids, including contact angle and surface tension. The final stage encompasses slot milling operations, where an array of parameters such as cutting forces, surface roughness, surface topography, surface texture, and the occurrence of burr formations are rigorously analyzed. The results demonstrate that the combination of multi-axial UVAM with HNMQL yields substantial advantages over traditional machining methods. Notably, it leads to a remarkable reduction in cutting forces (up to 37.6 %) and surface roughness (up to 37.4 %). Additionally, this combination engenders the production of highly homogeneous and uniform surface textures, characterized by minimal surface defects and a significantly diminished occurrence of burr formations. These findings underscore the potential of multi-axial UVAM combined with HNMQL as a promising approach in enhancing the machining of Ti-6Al-4V, thus offering a pathway to enhance the efficiency and precision of aerospace component manufacturing processes.
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    Estimation of the Mean Radiant Temperature in Office Buildings Using an Artificial Neural Network Developed in a Phyton Environment
    (Taylor & Francis Ltd, 2025) Ozbey, Mehmet Furkan; Lotfi, Bahram; Turhan, Cihan
    Thermal comfort describes an occupant's state of mind in a thermal environment, influenced by six parameters: air velocity, relative humidity, air temperature, mean radiant temperature (MRT), clothing value, and metabolic rate. MRT is the most problematic parameter since the obtaining process is difficult and time-consuming. MRT can be acquired by several methods such as calculations, measurements, assumptions, and software programmes. However, the methods have complexities and uncertainties. Comprehensive models are needed to obtain MRT. To this aim, this study presents an alternative method using one of the artificial intelligence methods, Artificial Neural Network (ANN), to predict MRT for indoor environments to abstain from the difficulties and complexities. A case building is selected in a university office building in Ankara, T & uuml;rkiye. The proposed model is developed and coded in a Python programming environment to predict the MRT using ANN. The results indicate that the ANN model, using only four inputs, predicts MRT with an R-2 value of 0.94 compared to the globe thermometer measurement method. The model's advantages over methods include simplicity, time efficiency and learning from the limited datasets such as difficulty in calculating terms like MRT.