TY - JOUR A1 - Rossi, Leonardo A1 - Stupazzini, Marco A1 - Parisi, Davide A1 - Holtschoppen, Britta A1 - Ruggieri, Gabriella A1 - Butenweg, Christoph T1 - Empirical fragility functions and loss curves for long-span-beam buildings based on the 2012 Emilia-Romagna earthquake official database JF - Bulletin of Earthquake Engineering N2 - The 2012 Emilia-Romagna earthquake, that mainly struck the homonymous Italian region provoking 28 casualties and damage to thousands of structures and infrastructures, is an exceptional source of information to question, investigate, and challenge the validity of seismic fragility functions and loss curves from an empirical standpoint. Among the most recent seismic events taking place in Europe, that of Emilia-Romagna is quite likely one of the best documented, not only in terms of experienced damages, but also for what concerns occurred losses and necessary reconstruction costs. In fact, in order to manage the compensations in a fair way both to citizens and business owners, soon after the seismic sequence, the regional administrative authority started (1) collecting damage and consequence-related data, (2) evaluating information sources and (3) taking care of the cross-checking of various reports. A specific database—so-called Sistema Informativo Gestione Europa (SFINGE)—was devoted to damaged business activities. As a result, 7 years after the seismic events, scientists can rely on a one-of-a-kind, vast and consistent database, containing information about (among other things): (1) buildings’ location and dimensions, (2) occurred structural damages, (3) experienced direct economic losses and (4) related reconstruction costs. The present work is focused on a specific data subset of SFINGE, whose elements are Long-Span-Beam buildings (mostly precast) deployed for business activities in industry, trade or agriculture. With the available set of data, empirical fragility functions, cost and loss ratio curves are elaborated, that may be included within existing Performance Based Earthquake Engineering assessment toolkits. KW - Empirical fragility functions KW - Empirical consequence curves KW - Precast buildings KW - Emilia-Romagna earthquake KW - PBEE Y1 - 2019 U6 - http://dx.doi.org/10.1007/s10518-019-00759-1 SN - 1573-1456 VL - 18 SP - 1693 EP - 1721 PB - Springer Nature ER - TY - JOUR A1 - Rossi, Leonardo A1 - Winands, Mark H. M. A1 - Butenweg, Christoph ED - Zhang, Jessica T1 - Monte Carlo Tree Search as an intelligent search tool in structural design problems JF - Engineering with Computers : An International Journal for Simulation-Based Engineering N2 - Monte Carlo Tree Search (MCTS) is a search technique that in the last decade emerged as a major breakthrough for Artificial Intelligence applications regarding board- and video-games. In 2016, AlphaGo, an MCTS-based software agent, outperformed the human world champion of the board game Go. This game was for long considered almost infeasible for machines, due to its immense search space and the need for a long-term strategy. Since this historical success, MCTS is considered as an effective new approach for many other scientific and technical problems. Interestingly, civil structural engineering, as a discipline, offers many tasks whose solution may benefit from intelligent search and in particular from adopting MCTS as a search tool. In this work, we show how MCTS can be adapted to search for suitable solutions of a structural engineering design problem. The problem consists of choosing the load-bearing elements in a reference reinforced concrete structure, so to achieve a set of specific dynamic characteristics. In the paper, we report the results obtained by applying both a plain and a hybrid version of single-agent MCTS. The hybrid approach consists of an integration of both MCTS and classic Genetic Algorithm (GA), the latter also serving as a term of comparison for the results. The study’s outcomes may open new perspectives for the adoption of MCTS as a design tool for civil engineers. KW - Monte Carlo Tree Search KW - Structural design KW - Artificial intelligence KW - Civil engineering KW - Genetic algorithm Y1 - 2022 U6 - http://dx.doi.org/10.1007/s00366-021-01338-2 SN - 1435-5663 SN - 0177-0667 VL - 38 IS - 4 SP - 3219 EP - 3236 PB - Springer Nature CY - Cham ER -