Population growth, economic development, and rapid urbanization in many areas have led to increased exposure and vulnerability of structural and infrastructure systems to hazards. Thus, developing risk-based assessment and management tools is crucial for stakeholders and the general public to make informed decisions on prehazard planning and posthazard recovery. To this end, structural risk and resilience assessment has been an ongoing research topic in the past 20 years. Recently, machine learning (ML) techniques have been shown as promising tools for advancing the risk and resilience assessment of structure and infrastructure systems. To date, however, there is a lack of a holistic review on ML progress across various branches of structural engineering; an in-depth analysis of literature that can provide a timely evaluation of risk and resilience assessment methods of the built environment, where different types of structural and infrastructure facilities are interconnected. For this reason, this study conducted a comprehensive review on ML for risk and resilience assessment in four main branches of structural engineering (buildings, bridges, pipelines, and electric power systems). To cover the crucial modules in the prevailing risk and resilience assessment frameworks, existing literature is thoroughly examined and characterized in terms of six attributes of ML, including method, task type, data source, analysis scale, event type, and topic area. Moreover, limitations and challenges are identified, and future research needs are highlighted to move forward the frontiers of ML for structural risk and resilience assessment.

Wood poles comprise a portion of the power utility's aging infrastructure that make a significant impact on customer's reliability. A large number may fail under the influence of severe wind storms and hurricanes, sometimes resulting in millions of dollars in replacement costs per year to the utilities. A holistic approach to risk assessment of wood poles in power distribution networks would therefore consider the simultaneous effects of decay and natural hazards on the failure probability of the poles. Toward this goal, this paper presents a framework for the development of age-dependent fragility curves of utility wood poles that relies on age-dependent probabilistic capacity models of wood poles and wind induced demand models. The framework considers uncertainties in the initial fiber strength of the wood poles, the age-dependent capacity loss model, the geometric features of wood poles of different classes, and the applied wind loads. The results of this study show that the decay process in wood poles may increase the fragility of the poles significantly as the age of the poles increase. The fragility curves provided in this research may constitute a major component of risk assessment approaches of power distribution networks against hurricanes and strong winds.