Search

Since destructive tests are not allowed for historical structures, numerical model updating using accelerometers has gained a lot of attraction in the last decade. Furthermore, another application of structural health monitoring is damage detection for near-real-time monitoring of cultural heritage assets of infrastructures such as masonry bridges. However, high cost is the main problem that discourages the use of large-scale structural health monitoring systems, and a modal pretest analysis is required to plan and optimize the modal tests procedure. For this purpose, various optimal sensor placement (OSP) techniques have been developed to derive the operational modal analysis results with a minimum number of sensors, leading to a lower cost. In this study, various OSP techniques have been applied to optimize sensor placement in two selected case studies. The first case study is a two-span masonry arch bridge in Rhodes, Greece and the second is a stone masonry tower located in Tønsberg, Norway. Baseline finite element models were developed before performing the ambient vibration tests and model updating process. The optimum sensor locations were detected using various techniques, and a comparative study was conducted on the results. Furthermore, the effect of considering soil-structure interaction on the OSP results was investigated.

Cultural heritage (CH) assets are the legacy of a society that are inherited from the past generations and can give us lessons for contemporary construction. Not only the formally recognized CH assets but also the non-CH structures and infrastructure, and the interconnection between them are crucial to be considered in a vulnerability assessment tool for the sustainable reconstruction of historic areas. Since most CH assets were not designed based on robust design codes to resist natural hazards such as earthquakes, vulnerability assessment and preservation are pivotal tasks for the authorities. For this aim, Hyperion, an H2020 project (Grant agreement No 821054), was formed in order to take advantage of existing tools and services together with novel technologies to deliver an integrated vulnerability assessment platform for improving the resiliency of historic areas. Geometric documentation is the first and most important step toward the generation of digital twins of CH assets that can be facilitated using 3D laser scanners or drone imaging. Afterward, the finite element method is an accurate approach for developing the simulation-based digital twins of cultural heritage assets. For calibration of the models, the result of the operational modal analysis from the ambient vibration testing using accelerometers can be utilized. Structural analysis for the prediction of the structural behavior or near real-time analysis can be carried out on the calibrated models. However, the full finite element analysis needs a lot of computational effort, and to tackle this limitation, equivalent frame methods can be utilized.

Global warming is one of the most important environmental issues that threatens the living on this globe so far. Carbon dioxide (CO2) emission from the construction industry is one of the major sources of emissions that leads to global warming. Therefore, CO2 emission reduction potentials are of additional attention nowadays. New technologies such as artificial intelligence, machine learning, and digital tools can assist this effort. Development in such technologies has made decision-making more optimized and automatic. To fulfil this aim, it is essential to have comprehensive knowledge on how emission reduction can be accomplished and what are the best decision-makings based on the new technologies of artificial intelligence and machine learning techniques. This paper provides a thorough picture of how artificial intelligence and machine learning techniques can contribute to CO2 emission reduction in construction. The concepts of CO2 reduction in the related derived literature are categorized into six clusters: 1) sustainable material design and production, 2) automation, digitalization and prefabrication, 3) real-world monitoring, 4) off-road vehicles and equipment, and 5) energy and life cycle assessment, and 6) decision-making and solution-based platforms. As most of the research studies in this area are related to the first cluster, i.e., sustainable material design and production, this paper has focused more on this area, and the other categories are preserved for future studies. Then, limitations and further research directions in this area are provided, which can be a valuable source for researchers in their future research.