Sameh Fuqaha
@umy.ac.id
Civil engineering
Muhammadiyah University of Yogyakarta
Sameh Fuqaha is a civil engineer and researcher specializing in sustainable construction materials, machine learning applications in civil engineering, and performance modeling of eco-friendly concrete systems. His work integrates artificial intelligence, statistical modeling, and experimental materials engineering to develop low-carbon and resource-efficient construction solutions. His research focuses on predicting mechanical and durability properties of sustainable concrete incorporating non-potable water, geopolymer binders, bentonite, and recycled materials. He is also active in bibliometric research, structural performance assessment, and AI-driven optimization of building materials.
EDUCATION
Master of Engineering in Civil Engineering (2024–2026)
Universitas Muhammadiyah Yogyakarta, Indonesia
Thesis: Machine learning and statistical modeling for sustainable concrete strength prediction using non-potable water, self-compacting geopolymer, and bentonite plastic concrete
Postgraduate Program in Environmental Studies (2020–2021)
Ben-Gurion University of the Negev, Israel
Thesis: Green Building Design for Arid Regions
Bachelor of Science in Civil Engineering (2013–2018)
Muğla Sıtkı Koçman University, Turkey
Erasmus+ Exchange: Politécnico da Guarda, Portugal
RESEARCH, TEACHING, or OTHER INTERESTS
Civil and Structural Engineering, Modeling and Simulation
FUTURE PROJECTS
I-Driven Mix Design Optimization for Low-Carbon Concrete
Development of hybrid machine learning and evolutionary optimization models to design concrete mixtures with reduced CO₂ emissions while maintaining structural performance.
Applications Invited
PhD students, data scientists, and materials engineers
Smart Monitoring of Concrete Structures Using AI and NDT
Integration of non-destructive testing (UPV, impact-echo) with machine learning for real-time structural health prediction of concrete infrastructure.
Applications Invited
Researchers in structural health monitoring, signal processing, and AI
Sustainable Use of Non-Potable Water in Construction
Experimental and AI-based modeling of durability and long-term performance of concrete made with treated wastewater and alternative water sources.
Applications Invited
Environmental engineers, civil materials researchers, and laboratory collaborators
Scopus Publications
Scopus Publications
Sameh Fuqaha
Engineering Perspective
This study presents the development and evaluation of an Artificial Neural Network (ANN) model for predicting the shear strength of reinforced concrete (RC) beam–column joints subjected to seismic loading. A comprehensive experimental database was compiled from more than 120 RC beam–column joint test specimens reported in the literature and used to train, validate, and test the ANN within MATLAB’s Neural Network Toolbox environment. The model employed the Levenberg–Marquardt backpropagation algorithm, a single hidden layer with an optimized number of neurons, a hyperbolic tangent sigmoid transfer function in the hidden layer, and a linear activation function at the output layer. Input parameters included concrete grade, reinforcement ratio, axial load, and joint geometry, while the output corresponded to joint shear strength. The ANN achieved outstanding predictive performance, with a coefficient of determination (R²) exceeding 0.99 and minimal error metrics (MSE = 0.000105), outperforming multiple regression models and ten widely adopted international design codes. Sensitivity analysis further revealed that reinforcement ratio and axial load were the most influential predictors of joint shear capacity. In addition to numerical prediction, the ANN demonstrated strong generalization capability and robustness across different concrete grades (M25–M40) and design standards. The results highlight the superior adaptability of machine learning compared to conventional design approaches, offering an innovative, data-driven framework for seismic performance assessment. This research contributes to the advancement of performance-based design methodologies by integrating artificial intelligence into structural engineering, paving the way for more accurate, efficient, and reliable seismic safety evaluations of RC joints.
Sameh Fuqaha and Ahmad Zaki
Yayasan Riset dan Pengembangan Intelektual
This study develops an interpretable machine learning framework to predict the mechanical properties of bentonite plastic concrete (BPC), an essential material for low-permeability geotechnical structures. Traditional testing of BPC is time- and cost-intensive, while empirical equations often fail to capture the nonlinear effects of bentonite and curing conditions. To address these limitations, four ensemble learning models were optimized using the Forensic-Based Investigation Optimization (FBIO) algorithm, a parameter-free metaheuristic inspired by investigative search processes. The models were trained on three curated experimental datasets to predict slump, tensile strength, and elastic modules. Among all, XGB–FBIO achieved the highest accuracy for slump (R² = 0.98) and tensile strength (R² = 0.99), while GBRT–FBIO performed best for elastic modulus (R² = 0.97). SHapley Additive exPlanations (SHAP) analysis revealed curing time, cement, and water content as the most influential variables. The results demonstrate that the proposed framework can replace repetitive laboratory trials with data-driven insights, providing engineers with a reliable, explainable, and resource-efficient tool for optimizing BPC mix designs in environmental and geotechnical applications.
Sameh Fuqaha, Ahmad Zaki, and Nursetiawan Nursetiawan
EDP Sciences
With growing concern over freshwater scarcity, this study explores the feasibility of using non-conventional water sources rainwater, underground water, mosque wastewater, and agricultural runoff as alternatives to potable water in concrete mixing. Five types of water were sourced locally and used in concrete mixtures designed using the ACI 211.1- 91 method with a constant water-to-cement ratio of 0.475. A total of 45 cylindrical specimens were tested for compressive strength at curing ages of 7, 14, and 28 days. The control mix using potable water achieved the highest 28-day strength (16.3 MPa), while concrete made with mosque wastewater and underground water reached 15.2 MPa and 14.7 MPa, respectively, demonstrating strong performance. Agricultural runoff showed acceptable results (14.1 MPa), suitable for non-critical applications. Rainwater, however, resulted in the lowest strength (8.5 MPa at 28 days), indicating potential issues with its chemical composition. The findings suggest that mosque wastewater and underground water are promising substitutes for structural-grade concrete, offering sustainable water reuse options in construction. This study provides practical insights for implementing alternative water strategies in water-scarce regions and underscores the need for further chemical and durability evaluations to support broader application.
Sameh Fuqaha, Ahmad Zaki, and Guntur Nugroho
Elsevier BV
Jazaul Ikhsan, Sameh Fuqaha, Adam P. Rahardjo, and Suharyanto
International Information and Engineering Technology Association
, Sameh Fuqaha, Ahmad Zaki, , Guntur Nugroho, and
MIM Research Group
As the construction industry intensifies its pursuit of sustainable and low-carbon alternatives to traditional materials, bamboo has emerged as a strategic resource owing to its rapid renewability, high strength-to-weight ratio, and superior environmental profile. This study presents a comprehensive bibliometric and thematic analysis of global research trends on bamboo construction, based on 1,456 peer-reviewed publications indexed in the Scopus database between 2019 and 2024. Using VOSviewer and the bibliometrix package in R, the analysis identifies three major thematic clusters: structural performance and mechanical behavior, sustainability and environmental applications, and microstructural characterization and advanced material development. The results highlight a sharp increase in scholarly output, with China, India, and Indonesia leading both in publications and citations, driven by strong policy initiatives and abundant bamboo resources. Despite significant advancements, critical gaps persist in standardization frameworks, long-term durability assessment, and integration with smart materials. This paper proposes future research pathways, including the development of hybrid composites, microstructural optimization, and regionally adaptive building codes. By systematically mapping the evolution, challenges, and opportunities in bamboo construction, the study offers a structured framework to accelerate bamboo’s mainstream adoption as a high-performance, sustainable material in global infrastructure development.
Sameh Fuqaha, Muhamad Umam, Septya Salsalbilla, Ahmad Zaki, Sri Atmaja, and Guntur Nugroho
Israa University - Gaza
Background: The structural integrity of basketball court concrete is critical for both safety and performance, yet systematic nondestructive evaluation (NDE) studies on thin-slab sports flooring remain limited. This research addresses that gap by applying the Impact-Echo (IE) method to assess material uniformity and detect potential flaws in basketball court concrete. Unlike most prior studies that emphasize frequency-domain analysis, this study integrates both Fourier Transform and Phase Angle behavior to enhance diagnostic reliability. Methods: Experimental testing was conducted with small and large hammers at sensor spacings of 10, 20, and 30 cm along three parallel lines of a full-scale court. Signals were processed using Fast Fourier Transform (FFT) and Phase Angle analysis to identify dominant frequency peaks, calculate thickness, and evaluate wave coherence. Results: Results show a clear inverse correlation between frequency and slab thickness, with Line 3 exhibiting the highest uniformity and Line 1 indicating localized variability. Phase Angle analysis revealed strong sensitivity to sensor distance and hammer size: small hammers with short spacing captured fine-scale anomalies, while large hammers and wider spacing yielded smoother generalized responses. To our knowledge, this is among the first studies to systematically evaluate basketball court concrete using both Fourier and Phase Angle analysis across varied sensor and hammer configurations. Conclusion: The findings refine understanding of how test parameters influence IE outcomes in thin concrete layers and provide a foundation for standardized, reliable NDE protocols that support quality control, safety assurance, and long-term monitoring of sports infrastructure.
Sameh Fuqaha, Ahmad Zaki, and Guntur Nugroho
IIUM Press
This research focuses on the predictive modeling of flexural (Ff) and splitting tensile (Ft) strengths in Self-Compacting Geopolymer Concrete (SCGC) to support sustainable mix design optimization. A curated dataset comprising 544 experimental records was utilized to train and evaluate eight supervised machine learning (ML) algorithms. These included Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forests, Gradient Boosting, CN2 Rule Induction, Naïve Bayes, Decision Trees, and Stochastic Gradient Descent. The predictive performance of each model was assessed using multiple statistical metrics, such as RMSE, R², and accuracy percentage. Among the models, SVM and KNN achieved the highest precision, with R² values of 0.99 and RMSE as low as 0.10 MPa. Additionally, statistical techniques were applied to identify influential input variables, confirming the dominant role of binder constituents in determining tensile-related strength. The models demonstrated strong generalization on unseen data and minimal sensitivity to activator dosage or curing age. These results validate the effectiveness of ML-driven tools for SCGC prediction and offer a scalable framework for integrating data analytics into sustainable concrete design and performance optimization. ABSTRAK: Kajian ini memfokuskan kepada pemodelan ramalan bagi kekuatan lenturan (Ff) dan tegangan belahan (Ft) dalam Konkrit Geopolimer Pemadat Kendiri (SCGC) bagi menyokong pengoptimuman reka bentuk campuran mampan. Satu set data terpilih yang merangkumi 544 rekod eksperimen telah digunakan bagi melatih dan menilai lapan algoritma pembelajaran mesin (ML) terselia. Algoritma tersebut termasuk Mesin Sokongan Vektor (SVM), K-Nearest Neighbors (KNN), Rawak Forests, Gradient Boosting, CN2 Rule Induction, Naïve Bayes, Pokok Keputusan, dan Stochastic Gradient Descent. Prestasi ramalan setiap model dinilai menggunakan pelbagai metrik statistik seperti RMSE, R², dan peratusan ketepatan. Antara model tersebut, SVM dan KNN mencapai ketepatan tertinggi dengan nilai R² sebanyak 0.99 dan RMSE serendah 0.10 MPa. Tambahan, teknik statistik turut digunakan bagi mengenal pasti pemboleh ubah input berpengaruh, sekali gus mengesahkan peranan dominan konstituen pengikat dalam menentukan kekuatan berkaitan tegangan. Model yang dibangunkan menunjukkan keupayaan generalisasi yang kukuh terhadap data baharu serta kepekaan minimum terhadap dos pengaktif atau umur pengerasan. Dapatan ini mengesahkan keberkesanan alat berasaskan ML bagi meramal SCGC dan menawarkan kerangka boleh skala bagi mengintegrasikan analitik data ke dalam reka bentuk konkrit mampan serta pengoptimuman prestasi.
SAMEH FUQAHA, Guntur nugroho, and Ahmad Zaki
University of Diyala, College of Science
This study presents a hybrid and interpretable modeling framework that integrates the Mori–Tanaka micromechanical model with artificial neural networks (ANNs) to predict the elastic modulus of bamboo-reinforced polypropylene composites. A synthetic dataset was created encompassing bamboo fiber volume fractions from 5% to 25%, enabling the ANN to generalize proficiently across diverse reinforcement setups. The ideal network architecture (2–15–1) attained superior predictive performance, with mean squared errors under 20 and regression coefficients surpassing 0.98, so validating the model's accuracy and robustness. To guarantee reliability, the model was evaluated on intermediate components not encountered during training, exhibiting consistent performance and resilience to overfitting. The interpretability of the black-box AI model was improved via sensitivity analysis and SHAP (Shapley Additive Explanations), which revealed that bamboo modulus was the primary factor affecting composite stiffness, contributing around 72% of predictive influence, whereas polypropylene accounted for 28%. These findings correspond with micromechanical theory and offer insights into material design methodologies. Combining physics-based modeling with artificial intelligence improves the accuracy of predictions and helps engineers make smart choices during the early stages of bio-composite development. This research enhances sustainable material innovation by offering a transparent, efficient, and scalable modeling tool suitable for comprehensive mechanical property forecasts and practical composite design.
, Sameh Fuqaha, Ahmad Zaki, , Guntur Nugroho, and
MIM Research Group
This study presents a simulation-based assessment of indoor air quality (IAQ) in residential buildings using bamboo-based construction materials, with a focus on volatile organic compound (VOC) emissions. Nine case studies were modeled using CONTAM software, examining variations in material selection, adhesive type, ventilation rate, and indoor temperature. The baseline case, using oriented strand board (OSB) and cork flooring, produced peak total VOC (TVOC) levels of 3.775 mg/m³ and formaldehyde concentrations up to 1.25 mg/m³, exceeding recommended thresholds despite sufficient ventilation. Replacing OSB and cork with laminated bamboo panels and flooring bonded with soy-based adhesives resulted in a 70–80% reduction in VOC emissions, with TVOC levels dropping to 0.88 mg/m³ and formaldehyde concentrations below 0.3 mg/m³. In contrast, bamboo bonded with melamine urea formaldehyde (MUF) adhesives showed moderate improvements, with TVOC at 1.02 mg/m³. Elevated indoor temperatures increased VOC levels by over 30%, while enhanced ventilation reduced them by 25–35%. Results from a mass balance analytical model aligned with simulation trends, supporting model validity. The findings demonstrate that combining low-emission bamboo materials with optimized ventilation offers a viable strategy for achieving healthier indoor environments, supporting sustainable and occupant-focused residential design.
RECENT SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Machine learning models for concrete strength prediction, bibliometric mapping tools for sustainable materials research, and AI-based structural performance prediction frameworks.
CONSULTANCY
Technical input on sustainable concrete materials and data-driven performance evaluation (academic and pilot research level).
INDUSTRY EXPERIENCE
Site Civil Engineer — Yilmazlar Construction Group (Turkey)
Experience in structural execution, material quality control, and construction project coordination.
STARTUP
Research contributes to low-carbon construction, water reuse in infrastructure, and AI-based performance optimization, supporting sustainable development goals in the construction sector.
SOCIAL, ECONOMIC, or ACADEMIC BENEFITS
Research contributes to low-carbon construction, water reuse in infrastructure, and AI-based performance optimization, supporting sustainable development goals in the construction sector.