Wulandari, Kiki Dwi (2026) Pemanfaatan Waste Material untuk Meningkatkan Kemampuan Self-sensing Beton pada Sistem Pemantauan Kesehatan Struktur. Doctoral thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Durabilitas dan serviceability struktur beton sangat dipengaruhi oleh kondisi material penyusunnya, sehingga diperlukan sistem pemantauan yang mampu mendeteksi perubahan perilaku struktural secara dini. Sensor konvensional sering menghadapi kendala durabilitas, kompatibilitas material, dan biaya pemeliharaan, sehingga konsep self-sensing concrete menjadi alternatif yang lebih efisien dan berkelanjutan. Penggunaan waste material seperti Ground Bottom Ash (GBA) yang berfungsi sebagai Supplementary Cementitious Material (SCM) sekaligus material semikonduktif menawarkan peluang untuk menghasilkan mortar dengan kemampuan sensing intrinsik. Penelitian ini mengembangkan mortar self-sensing berbasis GBA dengan karakterisasi kimia sesuai ASTM C618 serta pengujian mekanik dan elektrik pada kondisi tanpa beban, pembebanan kompresi monotonik, dan pembebanan lentur siklik. Pengujian tersebut dilakukan untuk mengevaluasi hubungan antara perubahan resistansi dan deformasi mekanik yang dihasilkan dengan mempertimbangkan parameter geometri spesimen dan konfigurasi elektroda yang mempengaruhi Electrode Affected Zone (EAZ). Kinerja sensing yang berasal dari bulk material kemudian dianalisis melalui empat indikator utama: Gauge Factor, Koefisien Variasi (CoV), Repeatability, dan Drift, serta dibandingkan dengan prinsip kerja strain gauge. Novelty penelitian ini adalah pembuktian empiris bahwa penambahan GBA secara signifikan meningkatkan kemampuan sensing mortar normal melalui perubahan kimia dan mikrostruktur yang membentuk jalur konduksi elektronik stabil. Peningkatan performa sensing tercermin dari nilai Gauge Factor yang lebih representatif, penurunan CoV dan Drift, serta Repeatability yang bagus. Evaluasi terpadu empat parameter tersebut menjadi kontribusi metodologis baru dalam kajian mortar self-sensing berbasis GBA. Hasil penelitian menunjukkan bahwa variasi 30% GBA memberikan keseimbangan paling optimal antara performa mekanik dan kemampuan sensing. GBA30 memiliki kuat tekan 20.88 MPa dan kinerja sensing yang lebih stabil (GF = 528; CoV = 8%; Repeatability = 0.367; Drift = 3.39%) dibanding mortar normal (Kuat tekan = 22.70 MPa; GF = 4470; CoV = 16%; Repeatability = 0.521; Drift = 7.11%). Dengan demikian, GBA30 menjadi kandidat paling layak untuk implementasi self-sensing mortar pada aplikasi Structural Health Monitoring System (SHMS).
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The durability and serviceability of concrete structures are strongly influenced by the condition of their constituent materials, necessitating a monitoring system capable of detecting early changes in structural behavior. Conventional sensors often suffer from limited durability, poor compatibility with cementitious matrices, and high maintenance costs, making self-sensing concrete a more efficient and sustainable alternative. The use of waste materials such as Ground Bottom Ash (GBA), which can simultaneously function as a Supplementary Cementitious Material (SCM) and a semiconductive component, offers a promising pathway for developing mortar with intrinsic sensing capabilities. This research develops a GBA-based self-sensing mortar through chemical characterization in accordance with ASTM C618, followed by mechanical and electrical testing under unloaded conditions, monotonic compression, and cyclic flexural loading. These tests were conducted to evaluate the relationship between electrical resistance changes and mechanically induced deformation while accounting for specimen geometry and electrode configuration, both of which influence the Electrode Affected Zone (EAZ). The sensing performance originating from the bulk mortar was assessed using four key indicators: Gauge Factor, Coefficient of Variation (CoV), Repeatability, and Drift, and benchmarked against the operational principles of strain gauges. The novelty of this study lies in the empirical demonstration that the incorporation of GBA significantly enhances the sensing performance of normal mortar through chemical and microstructural modifications that promote the formation of stable electronic conduction pathways. Improvements in sensing behavior are reflected in a more representative Gauge Factor, reduced CoV and Drift, and superior Repeatability. The integrated evaluation of these four parameters constitutes a methodological contribution to the development of GBA-based self-sensing cementitious materials. The findings indicate that a 30% GBA replacement level provides the most optimal balance between mechanical performance and sensing capability. The GBA30 mixture achieved a compressive strength of 20.88 MPa with more stable sensing performance (GF = 528; CoV = 8%; Repeatability = 0.367; Drift = 3.39%) compared with normal mortar (Compressive Strength = 22.70 MPa; GF = 4470; CoV = 16%; Repeatability = 0.521; Drift = 7.11%). Thus, GBA30 emerges as the most suitable candidate for implementing self-sensing mortar in Structural Health Monitoring System (SHMS) applications.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Ground Bottom Ash, Supplementary Cementitious Material, Self-Sensing Mortar, Performa Sensing, Sistem Pemantauan Kesehatan Struktur Ground Bottom Ash, Supplementary Cementitious Material, Self-Sensing Mortar, Sensing Performance, Structural Health Monitoring System |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) > TA1573 Detectors. Sensors T Technology > TA Engineering (General). Civil engineering (General) > TA418.16 Materials--Testing. T Technology > TD Environmental technology. Sanitary engineering > TD794.5 Recycling (Waste, etc.) T Technology > TH Building construction > TH880 Sustainable buildings. Sustainable construction. Green building |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Civil Engineering > 22001-(S3) PhD Thesis |
| Depositing User: | Kiki Dwi Wulandari |
| Date Deposited: | 30 Jan 2026 02:19 |
| Last Modified: | 30 Jan 2026 02:19 |
| URI: | http://repository.its.ac.id/id/eprint/131218 |
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