Advanced non-destructive techniques for the diagnosis of historic buildings: The Loka-Hteik-Pan temple in Bagan

Abstract

Cultural heritage is the foundation upon which global and historical values are based on. It connects us to the legacy left by our ancestors and identifies who we are as part of the modern society. Globally and specifically in the northeastern Romania, the landscape where cultural heritage sites were built on is constantly evolving due to mass wasting processes. Among these processes, landslide and gullies can disrupt the gravitational equilibrium directly or around these sites, threatening their very existence and our capacity to pass them on to future generations. Because landsliding and gullying are stochastic processes, the use of spatial statistics has often been employed to map locations at risk. In this work, we make use of advanced spatial Bayesian statistics to model landslide and gully erosion susceptibilities, separately. And, we ultimately combine these two outputs into a unified multi-hazard susceptibility model which we cross with the known cultural heritage sites in a study area close to the city of Iaşi, in Romania. Specifically, we implement a Bayesian version of a Generalized Additive Model (GAM) which assumes that the two separate landslide and gully presence/absence distributions to behave according to a Bernoulli probability distribution. Contrary to common practices in the literature, the two susceptibility models both feature fixed and random effects, including covariates acting at a latent level. We do this to also capture the unexplained but spatially coherent distribution of properties not directly included in the model. As for the properties directly expressed as covariates, our GAM features terrain attributes obtained from a LIDAR survey, in addition to land use and soil layers.The two single models outstandingly perform (AUC > 0.9) both during the calibration and validation phases. This modeling procedure ensures that the probability of occurrence of the two mass wasting processes under consideration is well estimated and therefore can be used to reliably plan conservation practices for local cultural heritage sites deemed at risk.

In-plane structural performance of dry-joint stone masonry Walls: A spatial and non-spatial stochastic discontinuum analysis

“…Furthermore, even when there is mortar between the units, weathering effects and lack of maintenance cause significant deterioration and often the total loss of the mortar, or at least of the loss of mortar-stone bond, over the lifetime of a structure, eventually rendering it similar to a dry-joint system. Recent and past earthquakes demonstrated that many of the unreinforced dry-joint and low-bond strength masonry structures are vulnerable to seismic actions and have been severely affected by past earthquakes [1][2][3]. Therefore, to better preserve, repair, and strengthen these structures, a thorough understanding of the DJ-SMW behavior under combined compression-shear forces is necessary.…”

Digital Twinning for 20th Century Concrete Heritage: HBIM Cognitive Model for Torino Esposizioni Halls

“…More specifically, it is an issue of harmonising in a single system geometric and radiometric data which characterise the architectural/structural elements, according to a classification that is significant for structural purposes and enriching them with the content of diagnostic tests which together aim at assessing the state of health of the building and anti-seismic characteristics. Even if the multi-sensor techniques offered by the market today are particularly versatile in terms of information density and accuracy and quite adaptable to the infinite variety of materials and configurations of architectural complexes [ 38 ], some challenging aspects of digitisation are directly related to the structural conception devised by PL. Nervi for Torino Esposizioni.…”

Research on Protection and Restoration of Heritage Buildings Based on Heritage Building Information Model (HBIM) Technology and Its Application

“…Multi-site scanning can be performed on the inside and outside of cultural relic buildings. The scanned data must undergo data noise reduction processing to obtain a complete point cloud data model for heritage buildings [13][14][15][16][17] ("Fig. 3");  Next, it is to survey and map the decorative lines and patterns of heritage buildings on the spot, and combine historical data, literature and social and cultural background and other survey data to build a decorative pattern library of heritage buildings.…”