A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Challenges and Opportunities for Concrete in the Digital Era
Concrete cannot be ignored. More concrete is produced than any other synthetic material on earth. It is the key material of our infrastructural systems, and together with mortar and other cementitious materials, it is one of the most generalizable answers to our global need for affordable and decent housing. Yet, present-day concrete is facing unprecedented challenges. In spite of an intrinsically low environmental footprint almost entirely due to its Portland cement content, it contributes an unacceptable 5–10% to our global carbon emissions, due to the enormous volumes involved. It suffers also from a poor social image, attached to its (supposedly) low technical content and its symbolic character for some of the least desirable aspects of modern societies and cities.
A major difficulty—which actually is also part of the solution—in the quest for a more sustainable and more acceptable concrete is the complex character of modern concrete, including its binder. The four-component (Portland cement–sand–aggregate–water) historical mixture has been replaced by a multicomponent and multi-scale granular, colloidal, and molecular composite with tunable properties. Simultaneously, a number of reinforcement methods have been developed, and new digital construction methods like slip casting or 3D printing for instance are nearing the point where they will be applicable on site.
In this context, the road toward sustainability and acceptability appears as a multifactorial optimal choice problem tackling simultaneously several so far separated ingredients: cement nature and composition, concrete formulation, architectural design, construction method, and finally recyclability at the material and building levels. Even if we restrict the problem to the formulation of concrete, the task is excessively difficult due to the width of the combinatorial space of possible constituents and the lack of fundamental knowledge on their interactions.
In this contribution it is suggested that, beside the now widely disseminated digitally assisted design (CAD) and management (BIM) methods in architecture and construction, the collection of big data (BD) sets on key aspects of concrete’s life and their subsequent analysis by machine learning (ML) may soon become privileged methods for the identification of sustainable material and technical choices. In particular, the recent progress in AI makes it reasonable to consider that, provided an extensive data collection network is established, ML may prove to be the most efficient concrete formulation method with a broad spectrum of target properties (carbon footprint, workability, structural buildup, hardening kinetics, ultimate strength, creep, recyclability, etc.) and input data (local availability of aggregates and supplementary materials).
Challenges and Opportunities for Concrete in the Digital Era
Concrete cannot be ignored. More concrete is produced than any other synthetic material on earth. It is the key material of our infrastructural systems, and together with mortar and other cementitious materials, it is one of the most generalizable answers to our global need for affordable and decent housing. Yet, present-day concrete is facing unprecedented challenges. In spite of an intrinsically low environmental footprint almost entirely due to its Portland cement content, it contributes an unacceptable 5–10% to our global carbon emissions, due to the enormous volumes involved. It suffers also from a poor social image, attached to its (supposedly) low technical content and its symbolic character for some of the least desirable aspects of modern societies and cities.
A major difficulty—which actually is also part of the solution—in the quest for a more sustainable and more acceptable concrete is the complex character of modern concrete, including its binder. The four-component (Portland cement–sand–aggregate–water) historical mixture has been replaced by a multicomponent and multi-scale granular, colloidal, and molecular composite with tunable properties. Simultaneously, a number of reinforcement methods have been developed, and new digital construction methods like slip casting or 3D printing for instance are nearing the point where they will be applicable on site.
In this context, the road toward sustainability and acceptability appears as a multifactorial optimal choice problem tackling simultaneously several so far separated ingredients: cement nature and composition, concrete formulation, architectural design, construction method, and finally recyclability at the material and building levels. Even if we restrict the problem to the formulation of concrete, the task is excessively difficult due to the width of the combinatorial space of possible constituents and the lack of fundamental knowledge on their interactions.
In this contribution it is suggested that, beside the now widely disseminated digitally assisted design (CAD) and management (BIM) methods in architecture and construction, the collection of big data (BD) sets on key aspects of concrete’s life and their subsequent analysis by machine learning (ML) may soon become privileged methods for the identification of sustainable material and technical choices. In particular, the recent progress in AI makes it reasonable to consider that, provided an extensive data collection network is established, ML may prove to be the most efficient concrete formulation method with a broad spectrum of target properties (carbon footprint, workability, structural buildup, hardening kinetics, ultimate strength, creep, recyclability, etc.) and input data (local availability of aggregates and supplementary materials).
Challenges and Opportunities for Concrete in the Digital Era
Bumajdad, Ali (editor) / Bouhamra, Walid (editor) / Alsayegh, Osamah A. (editor) / Kamal, Hasan A. (editor) / Alhajraf, Salem Falah (editor) / Van Damme, Henri (author)
Gulf Conference on Sustainable Built Environment ; Chapter: 3 ; 27-56
2020-04-08
30 pages
Article/Chapter (Book)
Electronic Resource
English
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