Teknisia

Evaluation of liquefaction potential in reworked volcanic-colluvial deposits of the Bawen Area, Semarang Regency

Desiana Vidayanti — 2025-11-30

Liquefaction is a major geotechnical hazard that can severely damage infrastructure in earthquake-prone areas. This study evaluates the liquefaction potential of volcanic–colluvial deposits in Semarang Regency, Central Java, using Standard Penetration Test (SPT) data and the Simplified Procedure of Seed and Idriss (1971). Cyclic Stress Ratio (CSR) and Cyclic Resistance Ratio (CRR) were computed to obtain Factors of Safety (FS) under three earthquake scenarios (Mw = 5.0, 5.9, and 6.5). Results show that for Mw = 6.5, the shallow sandy layers at 0-3 m have FS = 0.07-0.21 (highly susceptible), while the 4.5-9 m interval is FS = 0.8-0.96 (marginal to near-threshold) and and the >10 m strata remain stable (FS > 1.2). For Mw = 5.9, shallow liquefaction is confined to 0-3 m (FS = 0.09-0.27), with the 4.5-9 m zone showing FS = 1.0-1.2 (marginal to stable). Even for Mw = 5.0, the 0-3 m layer yields FS = 0.14-0.41, indicating liquefaction susceptibility, whereas deeper layers are stable (FS > 1.0-1.2). These findings indicate that loose, saturated silty-sand layers with shallow perched groundwater are the most critical to cyclic softening. The site is underlain by reworked volcanic-colluvial materials derived from Mount Ungaran, characterized by fine-grained, near-saturated deposits within the upper 10 m. Compared with previous studies in northern Semarang, this study highlights the moderate liquefaction susceptibility of southern volcanic-colluvial terrains, an area rarely analyzed in Central Java and provides practical insights for toll-road foundation design and mitigation strategies in similar geological settings.

Microzonation of soil resistance using shear wave velocity (Vs) for earthquake disaster mitigation in Singaran Pati District, Bengkulu City

Virgie Dhanty Kirana — 2025-11-30

Singaran Pati District, Bengkulu City, is located in an active subduction zone, making it prone to seismic activity. However, to date, there is no detailed microzonation map available to assess local vulnerability to earthquake shocks. This study aims to produce a microzonation map based on shear wave velocity (V_s) and Ground Amplification Factor (GAF) parameters as indicators of soil resistance. Vs values are calculated sequentially at depths of V_s10, V_s20, V_s30, V_s40, and V_s50 using spectral inversion methods. Soil site classification is based on NEHRP standards. Spatial interpolation uses the Inverse Distance Weighting (IDW) method to map parameter distribution. Results show that Vs velocity increases with depth, with a dominance of Class D sites (moderate soil), followed by Class C (very dense soil and soft rock), and a small portion of Class B (moderate rock). The GAF map identifies points with the highest amplification that are at high risk of damage due to earthquakes. The maximum Ground Amplification Factor (GAF) value is shown in red on the map, with a value of 2.0, while the minimum value is shown in green, representing a value of 1.0. This condition indicates that some areas in Singaran Pati Subdistrict have a significant potential for earthquake impact. Therefore, the use of microzonation maps is crucial as a basis for structural building planning and seismic risk mitigation in the Singaran Pati area.

Evaluating pavement condition using roadroid and Surface Distress Index (SDI): a case study of Klangon-Tempel Road, Yogyakarta Special Province

Fauji Ahmad — 2025-11-30

Acquiring accurate condition out of pavement evaluation can prove to be a challenge, particularly for local road agencies in Indonesia with limited resources. Conventionally, road condition is expressed in terms of the Surface Distress Index (SDI) and the International Roughness Index (IRI) that need time-consuming and labor-intensive road surveys. Advancements in smartphone technology has paved the way to a lower-cost and more rapid pavement evaluation by using applications such as Roadroids for IRI measurements. This study is aimed at exploring the viability of Roadroids-based IRI for pavement evaluation purposes. Klangon-Tempel road section in Yogyakarta Special Province was selected as the study area, on which a manual SDI and two Roadroid-IRI surveys were conducted. The two Roadroid surveys involved two different vehicle types: a sport utility vehicle (SUV) and a multi-purpose vehicle. The results showed that MPV-survey produced higher IRI values and were more consistent with pavement distresses observed through SDI survey, demonstrating a strong correlation coefficient of r=0.813. In contrast, SUV-survey showed significantly lower IRI values that overestimate overall pavement condition of the study area. No detailed investigation was made, but MPV features such as lower ground clearance and softer suspension system may contribute to cause the different outcomes. Complementing this, a range-based SDI–IRI analysis showed that SDI and IRI are consistent at low-distress levels but display substantial overlap across medium-to-high SDI categories, reflecting their inherently non-linear relationship. The findings suggests that, with appropriate type of vehicle, Roadroid can be a viable choice to conduct rapid IRI-based pavement evaluation, and thereby complement the traditional SDI surveys.

Optimizing ceramic waste content in concrete mix design: model development and android application

Andaryati Andaryati — 2025-11-30

There is an increasing need for practical tools that help users determine the appropriate mix composition of concrete incorporating ceramic waste as a partial fine aggregate replacement. Such tools can simplify the process of designing environmentally friendly concrete while ensuring that the resulting material meets the required strength standards. This study aims to determine the percentage of concrete mixture utilizing ceramic waste as a substitute for fine aggregates in concrete, assess its impact on concrete compressive strength using the RMSE method, and visualize it through Android modulator technology. Compressive strength tests were based on the previous research study, with curing intervals of 7, 14, and 28 days. The concrete quality test results indicate that concrete mixed with ceramic waste as a partial replacement for fine aggregates produces a strength that meets the standard for normal concrete, ranging between 30 - 40 MPa. A polynomial regression model synthesis based on the available laboratory data demonstrated good reliability, with an RMSE value of 0.172. Furthermore, using an Android emulator technology named BetonKU, the application successfully visualized the concrete mix design composition incorporating ceramic waste as a fine aggregate substitute according to the input data for the designed concrete strength.

Performance assessment of glass powder as filler in AC-WC asphalt exposed to Bekasi River Water

Ardhya Alga Lingga — 2025-11-30

Frequent flooding in Indonesia causes significant infrastructure damage, including deterioration of asphalt pavements. Bekasi City was selected as the study area due to its river system characteristics, which make the region highly susceptible to prolonged inundation. This study evaluates glass powder (GP) as an alternative filler in AC-WC mixtures and examines performance under simulated flooding. Five GP substitution levels (0%, 25%, 50%, 75%, and 100%) were tested and immersed in Bekasi River water for 0, 24, and 48 hours. Performance was assessed through Marshall Standard (MS), Indirect Tensile Strength ( ITS), Index of Retained Strength (IRS), Tensile Strength (TSR), and Cantabro Loss (CL) testing. Results show that GP affects all performance parameters, with 75% substitution exhibiting the best behavior among modified mixtures. However, all GP mixtures showed lower stability and ITS compared to the control, and full substitution (100%) resulted in the greatest performance decline. Longer immersion further reduced stability, ITS, TSR, and IRS while increasing flow, indicating higher moisture susceptibility. CL decreased with GP addition except at 100% substitution or extended immersion, where mass loss increased. Overall, while 75% GP substitution showed the best relative performance and remained functional up to 24 hours of immersion, long-term soaking led to significant degradation. GP shows potential as a sustainable filler, but improvements are required for use in flood-prone areas.

Implementation of Artificial Neural Network (ANN) for identifying design indicators of temporary modular shelters

Sely Novita Sari — 2025-11-30

The demand for fast, efficient, and adaptive emergency housing continues to increase, especially in disaster-prone areas and large-scale displacement situations. The determination of the design of Temporary Modular Shelter (TMS) so far still depends a lot on subjective considerations, so a more systematic and data-based approach is needed. This study develops and validates an Artificial Neural Network (ANN) model to identify the most suitable TMS design based on performance indicators and expert assessment. The approach was carried out through the Systematic Literature Review (SLR) stage, the determination of eight key design indicators, and assessment by 150 multidisciplinary respondents. The ANN model was built using a dense four-layer architecture with a total of 1,780 parameters and trained for 400 epochs using the TensorFlow and Keras libraries. The results showed a validation accuracy of 96% and a macro F1-score of 0,9146, indicating the stability and reliability of the model. Analysis of the contribution of features with the SHAP method revealed that the indicators of assembly methods, availability of human resources, and availability of local materials had the greatest influence on the classification results. This model has proven to be effective as a decision support system that is able to increase objectivity and efficiency in the TMS design process. Further development is suggested through integration into web-based digital platforms or mobile applications to support rapid and adaptive emergency response planning.