Acoustic Structures_ Material Research through Sonic Fingerprints and Data Visualization
(2026) ASEM01 20261Department of Architecture and Built Environment
- Abstract
- Architectural classification has long been constrained to visual standards, defining materials as flat surfaces or static geometric volumes. Yet, when dealing with reused materials, frequently non-standard and irregular, this surface-level consideration is not enough.
To surpass this visual bias, this thesis introduces a research workflow that interrogates the hidden identity of physical materials through their acoustic characteristics. By subjecting a heterogeneous set of waste and discarded architectural materials to systematic acoustic tests, internal resonance is treated as a primary source of material reality. This provides a future theoretical model for the field of urban mining - offering a non-destructive way to evaluate... (More) - Architectural classification has long been constrained to visual standards, defining materials as flat surfaces or static geometric volumes. Yet, when dealing with reused materials, frequently non-standard and irregular, this surface-level consideration is not enough.
To surpass this visual bias, this thesis introduces a research workflow that interrogates the hidden identity of physical materials through their acoustic characteristics. By subjecting a heterogeneous set of waste and discarded architectural materials to systematic acoustic tests, internal resonance is treated as a primary source of material reality. This provides a future theoretical model for the field of urban mining - offering a non-destructive way to evaluate salvaged resources whose structural conditions are unknown.
The sonic approach allows the material to detach from its superficial visual state, transforming physical matter into raw waves, frequencies, and numbers. From this translation, unexpected hidden physical relationships, similarities, and oppositions begin to reveal. Listening to the physical pieces transforms material analysis from an inspection into a dialogue with the material's inner structure.
The project culminates in a series of custom interactive interfaces that demonstrate the vital role of aesthetics in scientific research.
Rather than serving as passive illustrations of data, these act as filters to isolate meaningful material properties from mathematical noise. Working also with machine learning algorithms to cluster sonic fingerprints into relational patterns, these curated infographics translate chaotic acoustic signals into a legible geometric and topological language.
Ultimately, this thesis bridges the gap between raw engineering and architectural design.
By curating a multisensory framework to navigate and extract properties from our physical environment, the project expands architecture's traditional field of action, reframing the contemporary architect as also a material diagnostician. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9243424
- author
- Guedes, Filipe LU
- supervisor
- organization
- course
- ASEM01 20261
- year
- 2026
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- material reuse, data visualization, sonic ID, infographics, sonification, Rhino, Grasshopper, Python, interface
- language
- English
- id
- 9243424
- date added to LUP
- 2026-06-24 10:33:52
- date last changed
- 2026-06-24 10:33:52
@misc{9243424,
abstract = {{Architectural classification has long been constrained to visual standards, defining materials as flat surfaces or static geometric volumes. Yet, when dealing with reused materials, frequently non-standard and irregular, this surface-level consideration is not enough.
To surpass this visual bias, this thesis introduces a research workflow that interrogates the hidden identity of physical materials through their acoustic characteristics. By subjecting a heterogeneous set of waste and discarded architectural materials to systematic acoustic tests, internal resonance is treated as a primary source of material reality. This provides a future theoretical model for the field of urban mining - offering a non-destructive way to evaluate salvaged resources whose structural conditions are unknown.
The sonic approach allows the material to detach from its superficial visual state, transforming physical matter into raw waves, frequencies, and numbers. From this translation, unexpected hidden physical relationships, similarities, and oppositions begin to reveal. Listening to the physical pieces transforms material analysis from an inspection into a dialogue with the material's inner structure.
The project culminates in a series of custom interactive interfaces that demonstrate the vital role of aesthetics in scientific research.
Rather than serving as passive illustrations of data, these act as filters to isolate meaningful material properties from mathematical noise. Working also with machine learning algorithms to cluster sonic fingerprints into relational patterns, these curated infographics translate chaotic acoustic signals into a legible geometric and topological language.
Ultimately, this thesis bridges the gap between raw engineering and architectural design.
By curating a multisensory framework to navigate and extract properties from our physical environment, the project expands architecture's traditional field of action, reframing the contemporary architect as also a material diagnostician.}},
author = {{Guedes, Filipe}},
language = {{eng}},
note = {{Student Paper}},
title = {{Acoustic Structures_ Material Research through Sonic Fingerprints and Data Visualization}},
year = {{2026}},
}