Search

The analysis of particle size, shape, size, and their spatial arrangement (i.e. fabric) in a sedimentary deposit plays a significant role in their interpretation in terms of transport mechanism, flow conditions, and sediment source. The present work focuses on the use of X-ray computed tomography (X-CT) to analyze tsunami deposits (core samples), as examples of complex sedimentary assemblages. A workflow of image processing is proposed to measure 3D parameters of particle size, shape, and orientation using two different software packages (Fiji-ImageJ, and IPSDK) and two different methods of image segmentation. The semi-automated method provides better segmentation quality of the tsunami deposit, as compared with the poor performance of fully-automated image segmentation. We also provide some clues to reduce the computational time. This workflow is applied to two examples of tsunami deposits on the coasts of the Algarve (1755 Lisbon tsunami, southern Portugal). Optimized processing of X-CT images gives access to detailed vertical variations of grain size, grain shape, and componentry (clayey matrix, silicates, and carbonates, including marine bioclasts), as well as information on flow directions inferred from the sedimentary fabric in 3D. High-resolution analysis of the bedforms and vertical grain size grading allows determining the flow conditions during the inundation phase of the tsunami. • X-ray tomography is a revolutionary tool for conducting structural and textural analysis of tsunami deposits. • Image processing is challenging because particle of different type, size, and shape, are mixed in a muddy matrix. • The optimized semi-automated segmentation method developed here applies to all types of complex geological samples. [ABSTRACT FROM AUTHOR]

• Method for design repair mortars with specific mechanical and durability requirements. • Method for design of customized restoration mortars with aesthetic requirements. • Methodology for restoration of concrete heritage. The conservation of exposed concrete heritage implies repairs with restoration demands, besides mechanical and durability requirements. The main difficulty is to achieve chromatic, texture and finishing compatibility between repair mortars and concrete surfaces. The authors developed a restoration methodology for concrete heritage that comprises the chromatic characterization by image processing and the design of customized restoration mortars, fulfilling mechanical, durability, colour and finishing requirements. In this paper, the methodology is applied to a case-study, proving its ability to achieve the desired compatibility between the original surface and the restoration mortar, thus validating its use in safeguarding concrete heritage. [ABSTRACT FROM AUTHOR]

Single-layer cube (SLC) armoured breakwaters with a regular placement pattern are a relatively recent concept despite the cube being one of the oldest concrete units available. Limited investigation on the performance and hydraulic stability of this armour type is available to date. This work presents a quantitative assessment of the damage evolution for SLC armoured breakwaters with a regular placement pattern based on 2D physical modelling tests carried out at the University of Porto, Portugal. Tests were carried out for different armour porosities, wave steepnesses, and armour crest levels. A new method for automatically determining cube positions for each test was developed with image-processing techniques using advanced machine-learning tools. This method greatly reduces processing time since several images can be processed in batch mode. Innovative parameters to describe armour layer behaviour were derived and proposed for assessment and quantification of the damage evolution: exceedance probability of cube movements, mean grid orthogonality, and relative damage number for SLC. New damage level classification and criteria for initiation of damage are proposed for each of the parameters assessed. This analysis is focused on the initial stages of the damage, which are critical in designing new structures. [Display omitted] • 2D physical model tests for different armour porosities. • Automatic armour unit position detection using machine learning techniques. • Damage quantification through cube movements, grid orthogonality and N od-SLC. • Novel damage evolution classification and thresholds proposed. [ABSTRACT FROM AUTHOR]