Applications of resilience engineering principles in different fields with a focus on industrial systems: A literature review

Resilient performance in building maintenance: A macro-cognition perspective during sudden breakdowns

Building maintenance encompasses multiple tightly inter-connected agents (e.g., technicians, occupants, supervisors, and equipment). Variable working conditions and limited resources may affect the safety and sustainability of the activities. Although recent studies have explored how complex systems can perform resilient behavior in facing the complexity of everyday activities, the factors that effectively contribute to resilient performance are still paired with limited empirical evidence. We studied the performance of the maintenance team during sudden breakdowns of air-conditioning devices in a large university campus, using the Functional Resonance Analysis Method (FRAM). A FRAM diagram containing 30 functions was organized including six macro-cognitive functions (expertise, sensemaking, communication, coordination, collaboration, and adaptation/improvisation), examining their role in anticipating, and responding to emergencies, and eight functional units that are directly impacted by disturbances were analyzed in more detail. Results indicate that macro-cognitive functions can greatly impact the functionality of the maintenance team in pursuit of their goals. Moreover, we noted those macro-cognitive functions here analyzed depend on each other to produce resilient performance.

System resilience distribution identification and analysis based on performance processes after disruptions

Resilience is a system’s ability to withstand a disruption and return to a normal state quickly. It is a random variable due to the randomness of both the disruption and resilience behavior of a system. The distribution characteristics of resilience are the basis for resilience design and analysis, such as test sample size determination and assessment model selection. In this paper, we propose a systematic resilience distribution identification and analysis (RDIA) method based on a system’s performance processes after disruptions. Typical performance degradation/recovery processes have linear, exponential, and trigonometric functions, and they have three key parameters: the maximum performance degradation, the degradation duration, and the recovery duration. Using the Monte Carlo method, these three key parameters are first sampled according to their corresponding probability density functions. Combining the sample results with the given performance function type, the system performance curves after disruptions can be obtained. Then the sample resilience is computed using a deterministic resilience measure and the resilience distribution can be determined through candidate distribution identification, parameter estimation, and a goodness-of-fit test. Finally, we apply our RDIA method to systems with typical performance processes, and both the orthogonal experiment method and the control variable method are used to investigate the resilience distribution laws. The results show that the resilience of these systems follows the Weibull distribution. An end-to-end communication system is also used to explain how to apply this method with simulation or test data in practice.

Analysis of Geoecological Restoration in Mountainous Cities Affected by Geological Hazards with Interval Intuitive Fuzzy Information

With the progress of the industrial revolution and the development of modern science and technology, China's urbanization process has been promoted. Urban and rural economic and social construction has greatly improved the local appearance and social structure. Human activities and natural ecology have affected the whole geological-ecological process, further aggravated the geological-ecological damage, and caused more serious geological disasters, especially in some places (especially in mountainous areas). In recent years, strong geological disasters have occurred in Wenchuan, Yushu, and Lushan regions of China, which not only seriously endanger the life safety and social life of the affected people, but also damage the geological-ecological structure and social functions of the region, especially in the geographically sensitive Alpine urban areas. It also produced many secondary disasters, such as landslides and land collapses. Mountainous cities and towns have special requirements for construction land, which is difficult to construct. Industrial land resources are in short supply, urban and rural comprehensive construction land is not active, and cultivated land area resources are tight. Compared with plain towns with superior geological conditions, mountain towns are more vulnerable to adverse geological environment such as geological ecology, landform, ecological vegetation, and hydrology. The geographical natural environment, as an organic whole that combines and interacts with the geomorphic natural environment, the biological-ecological environment, and the human social management environment, is the main reason that affects the development of mountain towns. Once the mountain geological ecology is destroyed, a series of geological disasters will often be induced, which will seriously restrict the healthy development of mountain towns. Scientific management of the geological environment plays an important role in the assessment of the geological environment restoration of mountain towns after disasters. Therefore, taking the most beautiful counties in China, Baoxing City, and Tianquan County as examples, on the basis of studying the complex geological-ecological theory of geological disasters, this paper further improves the traditional ecological footprint model in China, and using the interval direct fuzzy information constructs the metric index of ecological restoration scheme of mountain towns, and determines the evaluation index and optimal scheme of ecological restoration. From the aspects of landscape layout construction, disaster prevention and mitigation planning and improvement, and environmental restoration project, the future geoecological restoration and response strategy of Lushan County are pointed out, which provide guidance for the postdisaster geoecological safety layout construction.

Analysis and Visualization of Research on Resilient Cities and Communities Based on VOSviewer

To objectively grasp the current situation and development trend of resilient cities or communities (RC) research. The articles in Web of Science (WOS) Core Collection databases from 1995 to 2022 were used as a sample, and bibliometrics was used to statistically analyze the year of publication and number of articles, highly cited documents and keyword hotness in this field. VOSviewer was used to explore the knowledge graph of RC research documents. The results show that: the development process is roughly divided into 3 periods: no attention (1995–2004), starting (2005–2014), and rapid growth (2015–2021). The journal “Sustainability” and “International journal of disaster risk reduction” are the key journals publishing RC research. Serre and Shaw are the most productive authors. The USA is still the leading country in this field of RC. Colorado State Univ, Texas a&m Univ, and Delft Univ Technol are the main research institutions. The keyword analysis indicates the hot topics in different periods. Moreover, several limitations and some recommendations for future research on RC are also given based on this.

System Resilience Evaluation and Optimization Considering Epistemic Uncertainty

Epistemic uncertainties, caused by data asymmetry and deficiencies, exist in resilience evaluation. Especially in the system design process, it is difficult to obtain enough data for system resilience evaluation and improvement. Mathematics methods, such as evidence theory and Bayesian theory, have been used in the resilience evaluation for systems with epistemic uncertainty. However, these methods are based on subjective information and may lead to an interval expansion problem in the calculation. Therefore, the problem of how to quantify epistemic uncertainty in the resilience evaluation is not well solved. In this paper, we propose a new resilience measure based on uncertainty theory, a new branch of mathematics that is viewed as appropriate for modeling epistemic uncertainty. In our method, resilience is defined as an uncertainty measure that is the belief degree of a system’s behavior after disruptions that can achieve the predetermined goal. Then, a resilience evaluation method is provided based on the operation law in uncertainty theory. To design a resilient system, an uncertain programming model is given, and a genetic algorithm is applied to find an optimal design to develop a resilient system with the minimal cost. Finally, road networks are used as a case study. The results show that our method can effectively reduce cost and ensure network resilience.