In an age where artificial intelligence is reshaping the landscape of education and problem solving, our study unveils the secrets behind three digital wizards, ChatGPT-3.5, ChatGPT-4, and Google Bard, as they engage in a thrilling showdown of mathematical and logical prowess. We assess the ability of the chatbots to understand the given problem, employ appropriate algorithms or methods to solve it, and generate coherent responses with correct answers. We conducted our study using a set of 30 questions. These questions were carefully crafted to be clear, unambiguous, and fully described using plain text only. Each question has a unique and well-defined correct answer. The questions were divided into two sets of 15: Set A consists of “Original” problems that cannot be found online, while Set B includes “Published” problems that are readily available online, often with their solutions. Each question was presented to each chatbot three times in May 2023. We recorded and analyzed their responses, highlighting their strengths and weaknesses. Our findings indicate that chatbots can provide accurate solutions for straightforward arithmetic, algebraic expressions, and basic logic puzzles, although they may not be consistently accurate in every attempt. However, for more complex mathematical problems or advanced logic tasks, the chatbots’ answers, although they appear convincing, may not be reliable. Furthermore, consistency is a concern as chatbots often provide conflicting answers when presented with the same question multiple times. To evaluate and compare the performance of the three chatbots, we conducted a quantitative analysis by scoring their final answers based on correctness. Our results show that ChatGPT-4 performs better than ChatGPT-3.5 in both sets of questions. Bard ranks third in the original questions of Set A, trailing behind the other two chatbots. However, Bard achieves the best performance, taking first place in the published questions of Set B. This is likely due to Bard’s direct access to the internet, unlike the ChatGPT chatbots, which, due to their designs, do not have external communication capabilities.

Bridge infrastructure has great economic, social, and cultural value. Nevertheless, many of the infrastructural assets are in poor conservation condition as has been recently evidenced by the collapse of several bridges worldwide. The objective of this systematic review is to collect and synthesize state-of-the-art knowledge and information about how bridge information modeling, finite element modeling, and bridge health monitoring are combined and used in the creation of digital twins (DT) of bridges, and how these models could generate damage scenarios to be used by anomaly detection algorithms for damage detection on bridges, especially in bridges with cultural heritage value. A total of 76 relevant studies from 2017 up to 2022 have been taken into account in this review. The synthesis results show a consensus toward the future adoption of DT for bridge design, management, and operation among the scientific community and bridge practitioners. The main gaps identified are related to the lack of software interoperability, the required improvement of the performance of anomaly-detection algorithms, and the approach definition to be adopted for the integration of DT at the macro scale. Other potential developments are related to the implementation of Industry 5.0 concepts and ideas within DT frameworks.

Among the most characteristic structures in historical constructions for crossing large spans are the masonry vaulted structures by utilizing their geometric stability to safely transfer the loads to supports with regard to their negligible tensile strength. The ability of masonry piers to bear such transferred stresses and safely convey them to the support is directly related to their structural integrity, as well as to a number of other factors. Using an in-house limit analysis code, a study on the crucial parameters impacting the safety level of piers under the thrust of arches is performed. Parameters such as pier texture, joint friction angle, and arch shallowness, namely, shallow, semi-circular, and pointed arches, were investigated under three load scenarios: horizontal and concentrated vertical live load applied at mid-span and quarter-span. The main findings of this work show that all studied parameters have a significant influence on the structure response. Higher friction values, sharper arches, and piers that follow the rule of art result in higher collapse multipliers. Furthermore, this work emphasizes the importance of accounting for the sliding mechanism and masonry texture, parameters that are often neglected.

Historical masonry structures have a great interest in civil engineering because they constitute a large part of the world’s building heritage. In this paper, the effects that different geometrical (panel ratio, block ratio, and bond type) and mechanical (friction ratio) parameters have on the in-plane structural response of brick masonry panels are investigated. A discrete modeling approach, based on a limit analysis and capable of reproducing sliding mechanisms, formulation by one of the authors has been adopted, enhanced, and implemented. Results, in terms of collapse multipliers and collapse mechanisms, are presented and analyzed following a systematic statistical approach. Statistically significant effects have been found for each factor considered. Furthermore, the statistical model adopted included nonlinear terms that allowed the identification of whether the effect of one parameter on the response depends on the level of any other parameters. Thus, it was observed that two-way factor interactions played an important role in the in-plane response of masonry panels. The panel ratio-friction ratio two-way factor interaction was the one with a more significant effect.