Abstract. Instantaneous bridge failure tends to increase in Indonesia. To mitigate this condition, Indonesia's Bridge Management System (I-BMS) has been applied to continuously monitor the condition of bridges. However, I-BMS only implements visual inspection for maintenance priority of the bridge structure component instead of bridge structure system. This paper proposes a new bridge failure prediction model based on hybrid Markov-System Dynamic (MSD). System dynamic is used to represent the correlation among bridge structure components while Markov chain is used to calculate temporal probability of the bridge failure. Around 235 data of bridges in Indonesia were collected from Directorate of Bridge the Ministry of Public Works and Housing for calculating transition probability of the model. To validate the model, a medium span concrete bridge was used as a case study. The result shows that the proposed model can accurately predict the bridge condition. Besides predicting the probability of the bridge failure, this model can also be used as an early warning system for bridge monitoring activity.

The implementation of construction projects always has the risk of work accidents. Improper execution of occupational health and safety can lead to various risks of work accidents. Therefore, it is necessary to research risk analysis and mitigation control to reduce the risk of work accidents. This study aims to determine the domi-nant risks related to Occupational Safety and Health (OSH) during the Corona Virus Disease 2019 pandemic. The method used in this study is Bowtie Analysis because this method can assess risk and maximize the mitigation of the causal factors and their impacts. The results showed five dominant risks caused by 20 causal factors and 14 effects of work accident risk. The chief risks include broken sling cranes, overturned cranes, workers falling during scaffolding installation, workers falling during painting, and workers falling from elevators. Meanwhile, the risk due to the COVID-19 pandemic almost did not affect the dominant risk of work-related accidents. The risk response resulting from this study contains 36 alternative mitigations to prevent and resolve risks.

Abstract. Bridges are one of the most important infrastructures which support the transportation system. It requires continuous monitoring to keep its condition and functionality. Bridge monitoring is used to support the maintenance strategy in order to prevent deterioration and sudden failure. This paper aims to propose a probabilistic prediction model of bridge conditions based on the Dynamic Bayesian Updating Approach. Around 3.166 data of bridges in Indonesia were collected from the Directorate of Bridges of the Ministry of Public Works and Housing for calculating the conditional probability table (CPT) of the model. A medium-span concrete bridge was used as a case study to validate the proposed model. The results show that the proposed model can predict the condition of the bridge accurately. It also can be used as an early warning system in order to prevent disasters due to technology failure.

Pada pelaksanaan pembangunan proyek seringkali terjadi masalah yang mengakibatkan proyek mengalami hambatan, sehingga menimbulkan keterlambatan waktu penyelesaian proyek dan kegagalan dalam mencapai tujuan proyek. Oleh karena itu, perlu dilakukan penelitian tentang analisis risiko dan langkah mitigasi yang efektif guna mengurangi dampak negatif dari keterlambatan (delay). Saat ini sudah banyak penelitian yang menganalisa risiko keterlambatan tetapi terbatas pada assessmen risiko berdasarkan probabilitas dan dampak saja dan mitigasi hanya dilakukan pada variabel penyebab tanpa memperhatikan agen keterlambatan. Sedangkan pada penelitian ini menggunakan metode House of Risk (HOR) yang terdiri dari 2 fase. Fase pertama (HOR-1) untuk menentukan faktor-faktor penyebab utama keterlambatan dan fase kedua (HOR-2) untuk memitigasi risiko secara efektif agar dapat menyelesaikan permasalahan keterlambatan dengan baik berdasarkan agen keterlambatan. Hasil analisis risiko keterlambatan menggunakan HOR diperoleh 6 kejadian keterlambatan (delay event) yang diakibatkan oleh 7 agen/penyebab keterlambatan dominan berdasarkan diagram pareto. Rekomendasi penanganan keterlambatan proyek yang efektif dapat dilakukan melalui 4 upaya mitigasi utama agar dampak keterlambatan dapat dicegah atau dikurangi.