Artificial intelligence-informed planning for the rapid response of hazard-impacted road networks

Post-hazard rapid response has emerged as a promising pathway towards resilient critical infrastructure systems (CISs). Nevertheless, it is challenging to scheme the optimal plan for those rapid responses, given the enormous search space and the hardship of assessment on the spatiotemporal status of CISs. We now present a new approach to post-shock rapid responses of road networks (RNs), based upon lookahead searches supported by machine learning. Following this approach, we examined the resilience-oriented rapid response of a real-world RN across Luchon, France, under destructive earthquake scenarios. Our results show that the introduction of one-step lookahead searches can effectively offset the lack of adaptivity due to the deficient heuristic of rapid responses. Furthermore, the performance of rapid responses following such a strategy is far surpassed, when a series of deep neural networks trained based solely on machine learning, without human interventions, are employed to replace the heuristic and guide the searches

Use of the Knowledge-Based System LOG-IDEAH to Assess Failure Modes of Masonry Buildings, Damaged by L'Aquila Earthquake in 2009

This article, first, discusses the decision-making process, typically used by trained engineers to assess failure modes of masonry buildings, and then, presents the rule-based model, required to build a knowledge-based system for post-earthquake damage assessment. The acquisition of the engineering knowledge and implementation of the rule-based model lead to the developments of the knowledge-based system LOG-IDEAH (Logic trees for Identification of Damage due to Earthquakes for Architectural Heritage), a web-based tool, which assesses failure modes of masonry buildings by interpreting both crack pattern and damage severity, recorded on site by visual inspection. Assuming that failure modes detected by trained engineers for a sample of buildings are the correct ones, these are used to validate the predictions made by LOG-IDEAH. Prediction robustness of the proposed system is carried out by computing Precision and Recall measures for failure modes, predicted for a set of buildings selected in the city center of L'Aquila (Italy), damaged by an earthquake in 2009. To provide an independent meaning of verification for LOG-IDEAH, random generations of outputs are created to obtain baselines of failure modes for the same case study. For the baseline output to be compatible and consistent with the observations on site, failure modes are randomly generated with the same probability of occurrence as observed for the building samples inspected in the city center of L'Aquila. The comparison between Precision and Recall measures, calculated on the output, provided by LOG-IDEAH and predicted by random generations, underlines that the proposed knowledge-based system has a high ability to predict failure modes of masonry buildings, and has the potential to support surveyors in post-earthquake assessments

Earthquake Damage Data Collection Using Omnidirectional Imagery

The unique perspectives and viewpoints offered by omnidirectional camera technology has the potential to help improve the outcomes of technical post-earthquake reconnaissance missions. Omnidirectional imagery can be used to virtually “walk through” damaged streets post hoc with a 360°, immersive view. A common reconnaissance mission aim is to accurately collect damage data; however, there are time challenges for surveyors in the field. The manuscript explores the potential for using omnidirectional imagery to improve damage surveying, firstly by comparing results from damage surveys completed in the field with results obtained using omnidirectional images collected during a mission and surveyed by an experienced engineer virtually and secondly by comparing damage assessment obtained through omnidirectional imagery collected on the ground with the EU Copernicus damage assessment maps. The omnidirectional imagery data was collected during two separate Earthquake Engineering Field Investigation Team post-earthquake reconnaissance missions, namely the area affected by the 2016, 7.8 Muisne Earthquake in Ecuador and the area struck by the 2016, 6.2 Amatrice earthquake in Italy. Notwithstanding the diverse geographic scale, terrain and urban context of the two reconnaissance missions, the results consistently show significant capabilities for this technology in the identification of construction typologies, number of stories, aggregated “low” and “high” damage grades, and failure modes. The work highlights potential issues with correct identification of disaggregated lower damage grades (e.g., European Macroseismic Scale (EMS-98) damage grades 0–3). Challenges identified in the virtual survey process included poor image quality, insufficient photo sphere captures, and obstructions such as trees, walls or vehicles. The omnidirectional imagery represents a substantial improvement in damage assessment accuracy in respect to satellite imagery, especially for lower damage grades, while it is an essential tool for comprehensive surveys in reduced access zones with high levels of damage

A Pan-European representative ground motion model

This poster is ESC2016-527 in the 35th General Assembly of the European Seismological Commission (Trieste, 4-10 September 2016)Ground motion prediction equations (GMPE) are recognised as a key component of any seismic risk analysis. The consideration of both aleatory and epistemic sources of variability in the ground motion models may have significant influence on the overestimation or underestimation of the final losses. With the increased availability of new developed GMPEs over the past few years, it has been observed that the epistemic uncertainty due the choice between potential GMPEs is not decreasing, even though related knowledge is improving.The proposed model enables a complex problem to be represented by a minimum number of branches for single-site hazard analysis and mapping. A preliminary application is carried out for a critical infrastructure risk analysis in the framework of the EU-funded INFRARISK project (European Commission’s FP7 programme, Grant Agreement No. 603960)Peer Reviewe

Transcending disciplines in architecture, structural and building services engineering: A new multidisciplinary educational approach

This paper reflects upon the mechanisms that enable development of curricular approaches to multidisciplinary architecture/engineering higher education. Building upon recent calls for integrated multidisciplinary building design practice, academics at UCL, industry partners and respective professional bodies embarked upon developing a new course that challenged disciplinary boundaries and defined the needs of a new design professional. Whilst there have been attempts internationally to better integrate architecture as well as engineering education, efforts have largely been focused on bolt-on solutions based on pre-existing education programmes. In addition, there has been little discussion (empirical or theoretical) on practical measures associated with developing multidisciplinary education in the built environment. Drawing on mixed data including documentary evidence, semi structured interviews and observations, the study begins to shed light on the approaches underpinning the development of a multidisciplinary built environment MEng course at UCL that integrates architecture, building services and civil engineering. The paper’s contribution is threefold. First, the findings have implications for developing multidisciplinary built environment education curricula, through revealing key mechanisms including the need for shared attitudes and expectations. Second, the paper highlights the conditions that enable the negotiation of multidisciplinary curricula including institutional support, shared values and a collective need and willingness to explore new solutions. Third, the paper reflects upon the value of design studio learning as a critical integrative component to the delivery of multidisciplinary education in the built environment and STEM more widely