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Abstract. The present study is focused mainly on development of the fragility curves for the sidesway collapse limit state. One important aspect of deriving fragility curves is how uncertainties are blended and incorporated into the model under seismic conditions. The collapse fragility curve is in uenced by di erent uncertainty sources. In this paper, in order to reduce the dispersion of uncertainties, Adaptive Neuro Fuzzy Inference System (ANFIS) based on the fuzzy C-means algorithm is used to derive structural collapse fragility curve, considering e ects of epistemic and aleatory uncertainties associated with seismic loads and structural modeling. This approach is applied to a Steel Moment-Resisting Frame (SMRF) structural model whose relevant uncertainties have not been yet considered by others in particular by using ANFIS method for collapse damage state. The results show the superiority of ANFIS solution in comparison with excising probabilistic methods, e.g., FirstOrder Second-Moment Method (FOSM) and Monte Carlo (MC)/Response Surface Method (RSM) to incorporate epistemic uncertainty in terms of reducing computational e ort and increasing calculation accuracy. As a result, it can be concluded that, in comparison with the proposed method rather than Monte Carlo method, the mean and standard deviation are increased by 2.2% and 10%, respectively.

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Nowadays, construction activities and projects are becoming much more challenging to manage.Being involve with different stakeholders is a difficult task and information and communication technology (ICT) has been addressed as a solution. On the other hand, Building Information Modeling (BIM) has already taken first steps towards this big revolution and gained a place in the sector. By inspiration from modality of BIM, there can be an adoption to other sub-branch of construction industry such as infrastructure domain. Since BIM has proven benefits in buildings, infrastructure projects might gain similar advantages through proper implementation. The authors of this paper are suggesting the use of a new describing for the purpose of BIM adoption for infrastructure projects, namely; Infrastructure Building Information Modeling (I-BIM). The main aim of conducting this research is to identify and prove the lack of using BIM for infrastructure projects.Within this context, questionnaire survey designed and distributed among 187 participants. These respondents were mostly located at United States of America and Turkey. As a finding of this research, authors clarify the preponderance and impediment components in the path of I-BIM utilization and discuss five main categories to clustered selected 26 variables as benefit or barrier of I-BIM.

Developing fragility functions is the essential step in incorporating important uncertainties in next-generation performance-based earthquake engineering (PBEE) methodology. The present paper is aimed to involve record-to-record variability as well as modelling uncertainty sources in developing the fragility curves at the collapse limit state. In this article, in order to reduce the dispersion of uncertainties, Group Method of Data Handling (GMDH) in combination with Monte Carlo Simulation (MCS) is used to develop structural collapse fragility curve, considering effects of epistemic and aleatory uncertainties. A Steel Moment Resisting Frame (SMRF) is chosen as the tested structure. The fragility curves obtained by the proposed method which belongs to GMDH approaches are compared with those resulted from simple and well-known available methods such as First-Order Second-Moment (FOSM), Approximate Second-Order Second-Moment (ASOSM) and Monte Carlo (MC)/Response Surface Method (RSM), somehow, as an accurate method. The results of the application of the proposed approach indicate increasing accuracy and precision of the outputs as well as power with the same computational time compared to aforementioned methods. The GMDH method introduced here can be applied to the other performance levels.

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