Sameh Fuqaha

@umy.ac.id

Civil engineering
Muhammadiyah University of Yogyakarta

Sameh Fuqaha


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https://researchid.co/samehfuqaha
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
12

Scopus Publications

38

Scholar Citations

4

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Debris flow prediction and impact mitigation: A comprehensive review and bibliometric perspective
    Jazaul Ikhsan, Sameh Fuqaha, Agung Setiawan, Yasunori Muto
    Multidisciplinary Reviews, 2026
    Debris flows are among the most destructive natural hazards in mountainous environments, and their increasing frequency under climate change and rapid land-use transformation has intensified the demand for accurate prediction and effective mitigation strategies. This study presents a comprehensive review of recent advances in debris-flow prediction and impact mitigation through an integrated bibliometric and technical analysis of 892 peer-reviewed publications indexed in Scopus from 2015 to 2025. The bibliometric results reveal a rapid growth in research output over the past decade, with East Asia and Europe emerging as major research hubs. Artificial intelligence, remote sensing, and early-warning systems are identified as dominant research frontiers. The technical synthesis demonstrates a clear methodological transition from traditional empirical and rainfall-threshold models toward hybrid frameworks that integrate physically based simulations, machine learning, and multi-source monitoring data. Deep learning and ensemble models consistently show superior predictive performance in susceptibility mapping, velocity estimation, and early-warning applications, while physically based multi-phase models remain essential for process understanding despite their high data and computational demands. The integration of GIS, UAVs, LiDAR, and real-time sensor networks has further enhanced spatial prediction accuracy and operational warning capability. This review also identifies critical scientific gaps in model transferability, uncertainty quantification, and data availability, and proposes future research directions focused on physics-guided machine learning, probabilistic early-warning systems, and climate-resilient mitigation strategies. The findings provide a structured scientific foundation for advancing next-generation debris-flow prediction and risk-reduction practices.
  • Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks
    Sameh Fuqaha
    Engineering Perspective, 2026
    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.
  • PREDICTING BENTONITE PLASTIC CONCRETE PERFORMANCE USING MACHINE LEARNING
    Sameh Fuqaha, Ahmad Zaki
    Journal of Applied Engineering and Technological Science, 2025
    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.
  • Performance of Concrete Mixed with Non-Potable Water Sources: A Compressive Strength Investigation
    Sameh Fuqaha, Ahmad Zaki, Nursetiawan Nursetiawan
    Bio Web of Conferences, 2025
    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.
  • A hybrid IVIF-MDL-MARCOS framework for sustainable selection of bamboo composites in green construction
    Sameh Fuqaha, Ahmad Zaki, Guntur Nugroho
    Advances in Bamboo Science, 2025
    The growing emphasis on sustainable infrastructure has amplified the demand for reliable frameworks to assess eco-friendly construction materials such as bamboo composites. These materials, recognized for their high strength-to-weight ratios, low biodegradability and renewability, offer a green alternative in civil engineering. However, selecting the optimal composite entails managing multiple conflicting criteria and expert uncertainty. We propose a novel hybrid decision-making framework integrating Interval-Valued Intuitionistic Fuzzy Sets (IVIF), Modified Digital Logic (MDL), and the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) method to assess and rank bamboo composites. The framework combines crisp technical indicators fuzzy sustainability assessments systematically. A case study involving 12 bamboo composite alternatives (B1–B12) was conducted, where expert skill weighting and linguistic evaluations were converted into IVIF numbers and defuzzified. The alternatives differed in their composition and performance characteristics. Some (B6, B11) exhibited higher compressive and flexural strengths with lower water absorption, while others (B9, B7) showed higher water uptake or lower biodegradability, reflecting trade-offs between mechanical efficiency and sustainability. The analysis identified B11 as the top-performing composite, offering the best compromise between mechanical performance (21.5 MPa compressive strength, 30.8 MPa flexural strength) and environmental merit (renewability and biodegradability > 0.90). Sensitivity and comparative validations against Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) confirmed the robustness and stability of the model. The proposed IVIF-MDL-MARCOS approach delivers a replicable, robust tool for sustainable material selection in green infrastructure. • A novel IVIF-MDL-MARCOS framework is developed for sustainable bamboo composite selection under uncertainty. • The model integrates quantitative performance metrics with fuzzy sustainability indicators using expert-weighted IVIFNs. • A real-world case study evaluates 12 bamboo composites for structural and environmental performance. • B11 emerged as the top-performing alternative with balanced mechanical strength and eco-friendly attributes. • Robustness of the model is validated through sensitivity analysis and cross-method comparison with TOPSIS and VIKOR.
  • Machine Learning for Rainfall-Driven Debris Flow Prediction in Data-Scarce Volcanic Watersheds
    Jazaul Ikhsan, Sameh Fuqaha, Adam P. Rahardjo, Suharyanto
    International Journal of Safety and Security Engineering, 2025
  • Bamboo in construction: Mapping global trends, strategic clusters, and future research pathways
    , Sameh Fuqaha, Ahmad Zaki, , Guntur Nugroho, and
    Research on Engineering Structures and Materials, 2025
    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.
  • Structural Assessment of Basketball Court Concrete Using Impact-Echo and Phase Angle Analysis
    Sameh Fuqaha, Muhamad Umam, Septya Salsalbilla, Ahmad Zaki, Sri Atmaja, Guntur Nugroho
    Israa University Journal of Applied Science, 2025
    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.
  • MACHINE LEARNING AND RSM FOR STRENGTH FORECASTING IN SUSTAINABLE SCGC
    Sameh Fuqaha, Ahmad Zaki, Guntur Nugroho
    Iium Engineering Journal, 2025
    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.
  • Interpretable AI-Based Prediction of Elastic Modulus in Bamboo-Reinforced Polypropylene Using Mori–Tanaka and Neural Networks
    SAMEH FUQAHA, Guntur nugroho, Ahmad Zaki
    Diyala Journal of Engineering Sciences, 2025
    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.
  • Optimizing indoor air quality in sustainable homes: A simulation-based evaluation of VOC emissions from bamboo-based materials
    , Sameh Fuqaha, Ahmad Zaki, , Guntur Nugroho, and
    Research on Engineering Structures and Materials, 2025
  • Compressive strength prediction of sustainable concrete incorporating non-potable water via advanced machine learning
    , Sameh Fuqaha, Ahmad Zaki, , Slamet Riyadi, and
    Sustainable Structures, 2025

RECENT SCHOLAR PUBLICATIONS

  • Artificial Intelligence for Sustainable Self-Healing Concrete Design Through Evolutionary Algorithms
    S Fuqaha
    An-Najah University Journal for Research-A (Natural Sciences) 40 (4) , 2026
    2026
  • Debris flow prediction and impact mitigation: A comprehensive review and bibliometric perspective
    J Ikhsan, S Fuqaha, A Setiawanb, Y Muto
    Multidisciplinary Reviews 9 (11), e2026544 , 2026
    2026
  • A risk-based cost estimation model for optimizing construction projects in Palestine
    S Fuqaha, I Farah, MH Zulfiar
    INTERNATIONAL JOURNAL OF INDUSTRIAL MANAGEMENT 20 (1), 21 – 36 , 2026
    2026
  • Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks
    S Fuqaha
    Engineering Perspective 6 (1), 19-32 , 2026
    2026
  • Predicting Bentonite Plastic Concrete Performance Using Machine Learning
    S Fuqaha, A Zaki
    Journal of Applied Engineering and Technological Science (JAETS) 7 (1), 679-707 , 2025
    2025
    Citations: 1
  • Compressive strength prediction of sustainable concrete incorporating nonpotable water via advanced machine learning
    S Fuqaha, A Zaki, S Riyadi
    Sustainable Structures 5 (4), 000092 , 2025
    2025
  • Visualization Analysis of Concrete Incorporating Waste Tire Rubber: A Knowledge Graph Approach
    S Fuqaha, J Ikhsan, A Zaki
    Journal of Civil Engineering 40 (3), 249–264 , 2025
    2025
  • Performance of Concrete Mixed with Non-Potable Water Sources: A Compressive Strength Investigation
    S Fuqaha, A Zakib, Nursetiawan
    2nd International Graduate Conference on Smart Agriculture and Green … , 2025
    2025
  • Machine Learning for Rainfall-Driven Debris Flow Prediction in Data-Scarce Volcanic Watersheds
    J Ikhsan, S Fuqaha, A Rahardjo, Suharyanto
    International Journal of Safety and Security Engineering 15 (11), 2379-2391 , 2025
    2025
  • A hybrid IVIF-MDL-MARCOS framework for sustainable selection of bamboo composites in green construction
    S Fuqaha, A Zaki, G Nugroho
    Advances in Bamboo Science, 100213 , 2025
    2025
  • Seismic Stiffness Evaluation of RC Dual Systems in Varying Geometries: A Pushover-Based Study Using Indonesian Codes
    S Fuqaha, G Nugroho
    Jurnal Teknik Sipil dan Perencanaan 27 (2), 49-61 , 2025
    2025
  • Review of Fly Ash-Based Zero-Cement Concrete Performance
    S Fuqaha, A Zaki, G Nugroho
    Jurnal Saintis 25 (2), 01-12 , 2025
    2025
  • Bamboo in construction: Mapping global trends, strategic clusters, and future research pathways
    S Fuqaha, A Zaki, G Nugroho
    Research on Engineering Structures and Materials 11 (5), 2581-2604 , 2025
    2025
    Citations: 2
  • Structural Assessment of Basketball Court Concrete Using Impact-Echo and Phase Angle Analysis
    S Fuqaha, M Umam, S Salsalbilla, A Zaki, S Atmaja, G Nugroho
    Israa University Journal of Applied Sciences 8 (2) , 2025
    2025
  • MACHINE LEARNING AND RSM FOR STRENGTH FORECASTING IN SUSTAINABLE SCGC
    S Fuqaha, A Zaki, G Nugroho
    IIUM Engineering Journal 26 (3), 53–88 , 2025
    2025
    Citations: 4
  • Interpretable AI-Based Prediction of Elastic Modulus in Bamboo-Reinforced Polypropylene Using Mori–Tanaka and Neural Networks
    S Fuqaha, G Nugroho, A Zaki
    Diyala Journal of Engineering Sciences 18 (3), 104-123 , 2025
    2025
    Citations: 4
  • Global Trends and Research Frontiers of Water Harvesting and Groundwater Recharge: A Comprehensive Bibliometric Review
    S Fuqaha
    Opportunities and Challenges in Sustainability 4 (2), 110-134 , 2025
    2025
    Citations: 1
  • Optimizing indoor air quality in sustainable homes: A simulation-based evaluation of VOC emissions from bamboobased materials
    S Fuqaha, A Zaki, G Nugroho
    Research on Engineering Structures and Materials 11 (4), 1767-1786 , 2025
    2025
    Citations: 5
  • Evaluating the Performance of Python-Based Machine Learning in Earthquake-Resistant Building Design
    S Fuqaha, G Nugroho
    Rekayasa Sipil 19 (2), 210–218 , 2025
    2025
    Citations: 1
  • Railway safety research: Mapping trends, strategic clusters, and future pathways
    S Fuqaha
    Journal of Railway Transportation and Technology 4 (1), 17–34. , 2025
    2025
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Artificial Intelligence and IoT for Smart Waste Management: Challenges, Opportunities, and Future Directions
    F Sameh, Nursetiawan
    Journal of Future Artificial Intelligence and Technologies 2 (1), 24-46 , 2025
    2025
    Citations: 17
  • Optimizing indoor air quality in sustainable homes: A simulation-based evaluation of VOC emissions from bamboobased materials
    S Fuqaha, A Zaki, G Nugroho
    Research on Engineering Structures and Materials 11 (4), 1767-1786 , 2025
    2025
    Citations: 5
  • MACHINE LEARNING AND RSM FOR STRENGTH FORECASTING IN SUSTAINABLE SCGC
    S Fuqaha, A Zaki, G Nugroho
    IIUM Engineering Journal 26 (3), 53–88 , 2025
    2025
    Citations: 4
  • Interpretable AI-Based Prediction of Elastic Modulus in Bamboo-Reinforced Polypropylene Using Mori–Tanaka and Neural Networks
    S Fuqaha, G Nugroho, A Zaki
    Diyala Journal of Engineering Sciences 18 (3), 104-123 , 2025
    2025
    Citations: 4
  • Railway safety research: Mapping trends, strategic clusters, and future pathways
    S Fuqaha
    Journal of Railway Transportation and Technology 4 (1), 17–34. , 2025
    2025
    Citations: 3
  • Bamboo in construction: Mapping global trends, strategic clusters, and future research pathways
    S Fuqaha, A Zaki, G Nugroho
    Research on Engineering Structures and Materials 11 (5), 2581-2604 , 2025
    2025
    Citations: 2
  • Predicting Bentonite Plastic Concrete Performance Using Machine Learning
    S Fuqaha, A Zaki
    Journal of Applied Engineering and Technological Science (JAETS) 7 (1), 679-707 , 2025
    2025
    Citations: 1
  • Global Trends and Research Frontiers of Water Harvesting and Groundwater Recharge: A Comprehensive Bibliometric Review
    S Fuqaha
    Opportunities and Challenges in Sustainability 4 (2), 110-134 , 2025
    2025
    Citations: 1
  • Evaluating the Performance of Python-Based Machine Learning in Earthquake-Resistant Building Design
    S Fuqaha, G Nugroho
    Rekayasa Sipil 19 (2), 210–218 , 2025
    2025
    Citations: 1
  • Artificial Intelligence for Sustainable Self-Healing Concrete Design Through Evolutionary Algorithms
    S Fuqaha
    An-Najah University Journal for Research-A (Natural Sciences) 40 (4) , 2026
    2026
  • Debris flow prediction and impact mitigation: A comprehensive review and bibliometric perspective
    J Ikhsan, S Fuqaha, A Setiawanb, Y Muto
    Multidisciplinary Reviews 9 (11), e2026544 , 2026
    2026
  • A risk-based cost estimation model for optimizing construction projects in Palestine
    S Fuqaha, I Farah, MH Zulfiar
    INTERNATIONAL JOURNAL OF INDUSTRIAL MANAGEMENT 20 (1), 21 – 36 , 2026
    2026
  • Seismic Shear Strength Prediction of RC Joints Using Shallow Neural Networks
    S Fuqaha
    Engineering Perspective 6 (1), 19-32 , 2026
    2026
  • Compressive strength prediction of sustainable concrete incorporating nonpotable water via advanced machine learning
    S Fuqaha, A Zaki, S Riyadi
    Sustainable Structures 5 (4), 000092 , 2025
    2025
  • Visualization Analysis of Concrete Incorporating Waste Tire Rubber: A Knowledge Graph Approach
    S Fuqaha, J Ikhsan, A Zaki
    Journal of Civil Engineering 40 (3), 249–264 , 2025
    2025
  • Performance of Concrete Mixed with Non-Potable Water Sources: A Compressive Strength Investigation
    S Fuqaha, A Zakib, Nursetiawan
    2nd International Graduate Conference on Smart Agriculture and Green … , 2025
    2025
  • Machine Learning for Rainfall-Driven Debris Flow Prediction in Data-Scarce Volcanic Watersheds
    J Ikhsan, S Fuqaha, A Rahardjo, Suharyanto
    International Journal of Safety and Security Engineering 15 (11), 2379-2391 , 2025
    2025
  • A hybrid IVIF-MDL-MARCOS framework for sustainable selection of bamboo composites in green construction
    S Fuqaha, A Zaki, G Nugroho
    Advances in Bamboo Science, 100213 , 2025
    2025
  • Seismic Stiffness Evaluation of RC Dual Systems in Varying Geometries: A Pushover-Based Study Using Indonesian Codes
    S Fuqaha, G Nugroho
    Jurnal Teknik Sipil dan Perencanaan 27 (2), 49-61 , 2025
    2025
  • Review of Fly Ash-Based Zero-Cement Concrete Performance
    S Fuqaha, A Zaki, G Nugroho
    Jurnal Saintis 25 (2), 01-12 , 2025
    2025

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.