This paper explores the transformative impact of synthetic biology (SynBio) on architecture, focusing on its role in construction materials. It argues that SynBio will influence construction through three significant waves: lab-grown biomaterials, bio-composites, and programmable bio-matter. The first wave involves the rise of lab-grown alternatives to traditional materials, such as bio-concrete, lab-grown wood, and mycelium-based materials. These materials, produced by companies like BioMason and Ecovative, offer sustainable options with reduced environmental impact, but it is argued that these are likely only the first manifestation of biological processes in material science.

The second wave introduces synthetic bio-composites, exemplified by synthetic spider silk and engineered wood. These materials, developed by companies like Spiber Inc. and Strong by Form, combine engineered biological materials with optimized forms to enhance performance beyond that of natural materials. The paper presents an exploration of string structures and algorithmic growth in architectural forms based on these materials and presents the wood construction work developed by the start-up Strong by Form.

The third wave explores programmable living materials (PLMs), which integrate living cells into scaffolds, enabling programmed functionalities such as self-assembly and self-growth. Through studio investigations, the paper presents a Bio-membrane Habitat design that leverages PLMs for a dynamic, self-assembling structure. The paper highlights that while challenges exist in scalability and cost-effectiveness, these innovations are ushering in a new era of architecture that is integrated with natural systems. The paper also emphasizes a shift in architectural thinking, moving from a model of large extraction present in traditional construction materials to one of biological self-growth, adaptation, and eventual reintegration with nature.

Even though buildings contribute 37% of global greenhouse gas emissions, the majority of efforts to reduce their GHG impacts have focused on reducing operational carbon emissions, while solutions for reducing embodied carbon of building materials lag behind. Bricks are a ubiquitous construction material utilized across the globe for their durability and excellent physical and mechanical properties. However, processes of manufacturing and curing most cementitious and clay bricks and plasters have a negative environmental impact, including damage to soil and workers’ health, high consumption of energy, and emission of GHGs.

Biochar is a charcoal-like material made from the thermal decomposition of biomass in the absence of oxygen at up to 800°C. Biochar can also sequester carbon and improve properties and net CO2 reductions in plasters and Portland cement-based concrete, respectively.

After extensive review of existing biochar studies, we conducted experiments in replacing cement, sand and lime with biochar in cementitious and clay mix designs for bricks and plasters. The biochar replacement mix recipes showed promising results and were focused on mix designs which include cement, lime, or clay. The mix designs were fabricated in two batches for testing. The first batch of samples (36) was fabricated with typical brick dimensions (3-⅝” x 2-¼” x 7-⅝”) utilizing a custom-designed reusable and adjustable mold for this project. The concrete bricks were tested for maximum compressive strength and three-point bending load capacities on both their horizontal and vertical axes. The second batch of samples (12) were fabricated as 1” thick plaster applications on two 6”x12” substrates (metal and wood furring strip lath), for testing. Six plaster mix designs applied to two different lath types. Common industry standard mix designs for lime, cement and clay bricks and plasters, without any biochar substitutions, served as a control for the qualitative and quantitative data collection process.

Engineered Living Materials (ELMs) combine synthetic biology and material science to mimic the properties of natural living materials. This new material category embeds living, genetically manipulated cells into a synthetic or biological matrix. Together, these components create a specialized functionality, such as self-strengthening or self-repair, to enhance the material properties. ELMs have been applied in regenerative medicine, therapeutics, electronics, device engineering, and computing; however, so far, few have been implemented as materials used in the built environment.

The authors of this study are part of a National Science Foundation (NFS)-funded interdisciplinary research team of chemists, biochemists, bioengineers, mechanical engineers, and architects that develop and investigate hybrid ELMs specifically for the applications in the BE. This paper focuses on the research by architects and engineers on this team to envision and test possible applications and implementation proposals of polymer-based, 3D-printed ELMs. The first round of material visioning and testing was supported by graduate students in architecture. Promising material ideas evolved in interdisciplinary discussions with chemists and mechanical engineers. These included self-hydration, self-nourishment, co-cultivation, self-strengthening, and the use of lattice structure. The material ideas advanced into parametric form finding, prototyping through additive manufacturing, and computational assessment.

This EFRI ELiS project demonstrates how essential interdisciplinary collaborations are for developing complex ELMs and materials innovations. It shows how architects and architectural students can take on crucial roles in advancing cutting-edge science with their domain knowledge in design, form-finding, material science, building construction, digital fabrication, and design thinking.

This research harnesses the power of machine learning to examine environmental disparities through an analysis of urban greenery in two distinct Cleveland neighborhoods: East Cleveland and Shaker Heights. The study investigates how artificial intelligence and computer vision techniques can shed light on the multidimensional nature of environmental inequalities in urban settings.

The methodological approach employs machine learning algorithms, including convolutional neural networks and unsupervised clustering techniques, to analyze high-resolution aerial imagery and street-level photographs. These advanced methods enable the quantification of various urban greenery aspects, including tree canopy distribution, vegetation density, green space fragmentation, color variations indicative of plant health and species diversity, and textural features suggestive of ecosystem complexity and biodiversity.

The findings unveil significant disparities between the two neighborhoods across multiple dimensions of urban vegetation. Shaker Heights emerges with notably superior tree canopy coverage, more uniformly distributed green spaces, and healthier vegetation characterized by a wider range of green hues and complex textures. Unsupervised clustering algorithms point to greater biodiversity in Shaker Heights, identifying a more diverse array of vegetation types and structures. In contrast, East Cleveland exhibits more fragmented green spaces and less diverse vegetation patterns. Notably, a robust correlation is observed between these urban vegetation characteristics and socioeconomic indicators, revealing an intricate interplay between neighborhood’s housing quality and environmental conditions.

This investigation showcases the effectiveness of machine learning in uncovering subtle urban environmental inequalities that extend beyond basic tree cover metrics. By capturing a comprehensive view of urban vegetation, including health indicators and density patterns this approach offers insights for urban planners and policymakers. It highlights the necessity for equitable urban green space policies that consider not only the quantity but also the quality and diversity of urban vegetation, emphasizing their integral connection to housing and community development strategies in urban areas.