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All Journal Jurnal Pensil : Pendidikan Teknik Sipil Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Pengabdian Masyarakat Konstruksi
Muharram Nur Fikri
Universitas Bengkulu
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Engineering Industrial & Manufacturing Engineering Mathematics Transportation Other

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ANALISIS PENGARUH PENAMPANG FONDASI TERHADAP DAYA DUKUNG DAN PENURUNAN FONDASI TIANG PANCANG BANGUNAN SCADA Raihanah Naura Jinan; Rena Misliniyati; Khairul Amri; Muharram Nur Fikri; Fepy Supriani
Jurnal Pensil : Pendidikan Teknik Sipil Vol. 14 No. 3 (2025): Jurnal Pensil : Pendidikan Teknik Sipil
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jpensil.v14i3.59178

Abstract

Bengkulu is an area with the potential risk of the Mentawai Pagai Megathrust subduction earthquake; it is necessary to evaluate the bearing capacity and foundation settlement of the building. This study aims to analyse the influence of variations in dimensions and cross-sectional shapes on the bearing capacity and foundation settlement of the Scada building using the Poulos and Davis method, the Reese and Wright method, the Luciano Decourt method, and the finite element method. Based on the results of the Standard Penetration Test (SPT), the influence of variations in shape, namely square and circular, with dimensions of 300 mm, 400 mm, and 500 mm, as well as depths of 7m, 9m, 11m, and 13m, affects the bearing capacity and foundation settlement. The analysis was conducted by comparing the bearing capacity and settlement of pile foundations in the Scada building using various methods. The analysis results show that the bearing capacity, deflection magnitude, and smallest settlement are below the permitted settlement limit, i.e., less than 10% of the foundation dimensions. The comparison between static and numerical methods, or the Bearing Capacity Ratio (BCR) approaching 1, is more efficient and safer to use. In this analysis, the BCR value closest to 1 was obtained for a 500 mm foundation using the Reese and Wright method at a depth of 9 m, yielding a bearing capacity of 312.04 tonnes for a single pile and 207.69 tonnes for a pile group.
Sosialisasi Mitigasi Gempa dan Tsunami bagi Masyarakat Desa Sido Urip, Kabupaten Bengkulu Utara Lindung Zalbuin Mase; Muharram Nur Fikri; Aidil Fitriansyah
Jurnal Pengabdian Masyarakat Konstruksi Vol 4 No 1 (2026): Majjama - Jurnal Pengabdian Masyarakat Konstruksi
Publisher : PSTS FT UIM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63877/jpmk.v4i1.265

Abstract

Provinsi Bengkulu berada pada zona subduksi Sumatra yang aktif secara seismik, sehingga masyarakatnya rentan terhadap ancaman gempa bumi dan tsunami. Masyarakat Desa Sido Urip, Kecamatan Arma Jaya, Kabupaten Bengkulu Utara, masih memiliki keterbatasan pemahaman mengenai mekanisme gempa, potensi tsunami, dan langkah mitigasi sederhana. Kegiatan pengabdian ini bertujuan meningkatkan pengetahuan dasar dan kesadaran mitigasi bencana melalui sosialisasi tatap muka dan diskusi interaktif. Metode pelaksanaan meliputi penyampaian materi berbasis kajian seismotektonik Bengkulu, penyuluhan di balai desa, sesi tanya jawab, serta evaluasi kualitatif melalui observasi partisipasi peserta. Kegiatan dilaksanakan pada 14 Juli 2025 dengan melibatkan 40 peserta dari kalangan perangkat desa, masyarakat umum, dan mahasiswa KKN. Hasil menunjukkan antusiasme tinggi yang tercermin dari banyaknya pertanyaan kontekstual dan komitmen peserta untuk menyebarluaskan informasi kepada keluarga dan tetangga. Kegiatan ini mengindikasikan bahwa sosialisasi berbasis dialog efektif sebagai langkah awal pembentukan budaya sadar bencana di tingkat desa, meskipun pengukuran kuantitatif dan program lanjutan yang lebih sistematis masih diperlukan.
Integrating Finite Element Analysis and Machine Learning to Predict the Bearing Capacity of Strip Footings on Slopes Aditya Dwi Kurniawan; Lindung Zalbuin Mase; Muharram Nur Fikri; Rena Misliniyati; Aidil Fitriansyah
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 2 (2026): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i2.16161

Abstract

Predicting the ultimate bearing capacity (qult) of strip footings on slopes remains a major challenge in geotechnical engineering, as classical methods were developed for level ground and lose reliability for complex slope-foundation geometries. Although finite element analysis (FEA) provides better accuracy, its high computational cost limits large-scale parametric studies. This study proposes a hybrid FEA–machine learning (ML) framework to estimate qult of strip footings on slopes, overcoming the accuracy limitations of existing analytical solutions for different slope-foundation configurations. The dataset of 600 finite element simulations was developed under the Mohr-Coulomb plane strain constitutive framework. Six variables were examined: unit weight (γ), cohesion (c), friction angle (φ), applied load (P), foundation width (B), and embedment depth (Df). Seven predictive models were developed: multiple linear regression, polynomial regression, support vector regression, decision trees, random forests, k-nearest neighbors, and extreme gradient boosting (XGBoost). Model performance was assessed using R², RMSE, MAPE, and the a20 index, with R² and RMSE as the primary ranking criteria, while Shapley Additive Explanations (SHAP) were applied to interpret feature contributions. XGBoost has the highest prediction accuracy on both the training and test datasets. It is followed by Support Vector Regression (SVR). The most influencing parameter in all seven models was the foundation depth (Df), followed by the friction angle (φ) and the foundation width (B), while the slope angle consistently decreased the predicted bearing capacity. The results confirm the accuracy, interpretability, and computational efficiency of the integrated FEA-ML approach as an alternative to traditional bearing capacity analysis.
Co-Authors Aditya Dwi Kurniawan Aidil Fitriansyah Fepy Supriani Khairul Amri Lindung Zalbuin Mase Raihanah Naura Jinan Rena Misliniyati