Cantilever: Jurnal Penelitian dan Kajian Bidang Teknik Sipil

Perbandingan Waktu Proses Perencanaan Gedung Bertingkat Antara Menggunakan Dynamo dan Metode Konvensional pada Revit

Yoga Kencana, Toriq Arif Ghuzdewa, Akhmad Aminullah — Mon, 08 Sep 2025 23:53:05 +0700

Building Information Modeling (BIM) is a computer-based technology that facilitates planning activities across various disciplines, including structural, architectural, and mechanical–electrical works. The implementation of BIM in construction projects requires a high level of accuracy to ensure cost efficiency and smooth workflow. This study aims to examine the time differences in material calculation processes using two different methods: the conventional Material Take-Off feature in Autodesk Revit and a custom plug-in developed using Dynamo. The study employs a 3D model of a three-story building as the test object. Work duration was measured using a stopwatch at the beginning of each calculation process to obtain more precise time records. The results indicate that the use of Dynamo significantly accelerates the calculation process, achieving a fivefold speed improvement compared to the conventional Material Take-Off method. Furthermore, the developed Dynamo script can be applied to projects of the same type but with different geometric forms. Therefore, the proposed script is expected to serve as a practical solution for enhancing accuracy and productivity in concrete work planning for BIM-based construction projects.

In Situ Soybean Crude Urease Calcite Precipitation (SCU-CP) untuk Perkuatan Tanah Pasir

Tue, 09 Sep 2025 00:16:24 +0700

Sand has low bearing capacity due to its low shear strength and lack of cohesion. Chemical improvement is more effective than physical methods for reinforcing sand, as it does not require heavy equipment. Enzyme-Mediated Calcite Precipitation (EMCP) is a chemical method that uses pure urease enzyme, but it is not economical for field applications. Therefore, the Soybean Crude Urease Calcite Precipitation (SCU-CP) method, which substitutes pure urease with soybeans, is used as a solution. This study applies SCU-CP to reinforce sand at both laboratory and field scales using Cone Penetration Test (CPT) and Unconfined Compression Strength (UCS), with variations of 1, 2, and 3 pore volumes (PV). The study began with testing sand parameters, including gradation, specific gravity, and void ratio. Treated sand samples were tested using CPT and UCS at both scales. Calcite quantification was conducted to determine the amount and distribution of calcite formed. The results show that SCU-CP increased qc values in coarse and fine sand in the CPT test, with the highest values at 3 PV being 1.28 MPa and 2.01 MPa, respectively. Laboratory UCS tests showed maximum values at 3 PV of 331.13 kPa for coarse sand and 104.38 kPa for fine sand. Field UCS tests showed values of 193.19 kPa for coarse sand and 210.84 kPa for fine sand. Calcite distribution was uniform, with actual calcite content reaching 27.66 - 34.10% of the theoretical mass.

Smart Contracts in the Construction Industry: A New Era of Transparency and Efficiency

Ryan Adiputera, Fuk Jin Oei — Tue, 09 Sep 2025 00:28:46 +0700

Construction industry often faces challenges such as payment delays, contract disputes, and a lack of transparency and administrative efficiency. Smart contract technology based on blockchain offers an innovative approach to address these issues through transparent, automated, and tamper-resistant systems. This study explores various applications of blockchain and smart contracts in construction projects, including solutions for automated progress payments, digital contract management, quality improvement, and technology-based dispute resolution, using the Systematic Literature Review (SLR) method. Smart contract systems can execute payments automatically based on verified work progress, while blockchain technology ensures secure and immutable data recording. Additionally, integration with technologies such as Building Information Modeling (BIM) and the Internet of Things (IoT) enhances real-time project management. Despite offering numerous benefits, the implementation of these technologies still faces challenges such as regulatory uncertainty, infrastructure readiness, and the need for standardization. This study highlights that the adoption of blockchain and smart contracts holds significant potential to drive efficiency, fairness, and digital transformation in the construction sector.

Post-Earthquake Bridge Damage Assessment Using Machine Learning (ML) and Artificial Inteligence (AI): A Systematic Literature Review

Ainil Mardhiyah — Mon, 27 Oct 2025 13:15:11 +0700

Bridges are critical infrastructure highly vulnerable to earthquake-induced damage, posing serious risks to transportation continuity and public safety. Traditional methods, such as visual inspection, remain in use; however, they are limited in efficiency, scalability, and accuracy. This highlights the urgent need for more advanced approaches. Machine Learning (ML) and Artificial Intelligence (AI) have emerged as promising alternatives for assessing post-earthquake bridge damage. However, existing studies often lack a systematic synthesis of methodological trends, rely on limited or unstandardized datasets, and insufficiently address real-world implementation challenges. This study conducts a Systematic Literature Review (SLR) to critically examine the application of ML/AI in post-earthquake bridge damage assessment, focusing on methodological trends, commonly used datasets, and implementation challenges. Relevant journal articles published between 2019 and 2025 were selected through structured keyword strategies and filtered based on publication type, relevance, and journal quality (Q1-Q4). The findings indicate that Random Forest (RF) and Convolutional Neural Networks (CNN) are among the most widely applied ML methods, owing to their strengths in classification and visual data analysis. Frequently used datasets include bridge damage records from California, shake table test time-series data, and sensor-based monitoring data. Persistent challenges include data heterogeneity, limited availability of real-time datasets, and the interpretability of ML models. The novelty of this study lies in providing a consolidated synthesis of current research, bridging methodological gaps, and highlighting implementation challenges. Future research should focus on developing real-time datasets, establishing robust model validation frameworks, and enhancing the interpretability of ML techniques to strengthen disaster risk mitigation and improve bridge resilience.

Evaluasi dan Pemetaan Tingkat Resiko Longsor di Sumatera Selatan

Puspita Epianti, Bulkin Fathoni, Nurly Gofar — Mon, 27 Oct 2025 14:41:08 +0700

South Sumatra Province is situated in the southern part of Sumatra Island. The western part of this province rests on the Bukit Barisan Mountains. National roads in this area pass through steep slopes and valleys, making them prone to landslides. This study aims to evaluate the slope risk on twelve (12) national road segments crossing the Bukit Barisan area. The study was conducted based on data collected by the South Sumatra Province National Road Implementation Center (BBPJNSS) in 2018 - 2022. The risk level is calculated and classified based on the Road Slope Risk Assessment Guidelines provided by the Ministry of PUPR in 2017. The evaluation results showed that the most vulnerable road segments to landslides are Road segments 15-036, 15-37, and 15-38, connecting Lahat, Pagar Alam, Tanjung Sakti, and the border of Bengkulu Province. Meanwhile, the most prominent types of landslides are soil and rock collapse. The highest number of slopes with high and very high risks occurred in 2020; thereafter, the number declined in 2021 and 2022, as the Ministry of Public Works initiated mitigation projects in the area. The landslide risk map created in this study shows that in 2022, areas with a high risk of landslide have been reduced by the systematic mitigation program implemented by PUPR based on the slope inventory carried out since 2018.

Preliminary Assessment of Building Damage Potential Due to Landslide (Case Study Gua Maria Kerep Ambarawa, Central Java)

Thu, 04 Dec 2025 16:40:58 +0700

Landslides are complex phenomena that are frequently interpreted differently across disciplines due to their mechanisms and impacts on both nature and society. This paper presents an assessment of landslide vulnerability in the Gua Maria Kerep Ambarawa (GMKA) area, a Catholic pilgrimage site in Central Java, Indonesia. The study highlights the need to identify and classify disaster-prone areas to facilitate effective regulation, site development, and disaster mitigation efforts. Focusing on GMKA, the research employs a phased vulnerability assessment approach to map potential sources of landslide hazards, particularly following a surface landslide during the early 2024 rainy season. The observations indicate that water seepage and soil saturation around the graveyard area were key contributing factors. The movement of soil under the surface was detected using georadar, which measures the shear wave velocity of the soil layer under the surface. Visual indicators, such as gaps in masonry, can serve as an effective hazard prevention measure prior to conducting instrumental inspections.