Worthy, A., Ashayeri, M., & Abbasabadi, N. (2025). Leveraging earth observational data products and machine learning to enhance urban building energy modeling (UBEM) with microclimate effects. Sustainable Cities and Society, 130, Article 106544. https://doi.org/10.1016/j.scs.2025.106544.
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Abstract
Urban Building Energy Modeling (UBEM) is a powerful tool used for sustainable design, urban planning, and efficient energy management, as it provides essential insights into the building energy consumption patterns. However, current UBEM methodologies often lack urban-specific microclimate data, leading to discrepancies between modeled and actual energy consumption. This research develops a bottom-up statistical UBEM framework that combines and integrates earth observational climate data, climate reanalysis products, and annual energy usage data, measured by the Seattle Energy Benchmarking Dataset, to capture the impacts of microclimates on urban building energy performance. Using machine learning techniques and Seattle, Washington, USA as a proof of concept, our results demonstrate that incorporating urban-specific microclimate data substantially enhances building energy modeling prediction accuracy. Specifically, three model variable schemas are compared; the optimal model incorporating earth observational data achieved a 0.16 (from 0.55 to 0.71) increase in testing R2 over the model that did not include any climate data inputs, and a 0.056 (from 0.66 to 0.71) increase in testing R2, over the model that included TMY3 climate data inputs. These findings validate the use of earth observational datasets for urban building energy modeling to include microclimate effects. Furthermore, machine learning algorithms outperform traditional linear methods, with respective ordered rankings: CATBoost, XGBoost, Random Forest, Decision Trees, and Linear Regression. Our study underscores the importance of integrating microclimate data into UBEM frameworks and advocates for the expanded use of earth observational and remote sensing datasets for mitigation of simulation-to-reality discrepancies; to ultimately inform more accurate energy-driven design and planning strategies at the city level.
Tabatabaei Manesh, M., Rajaian Hoonejani, M., Ghafari Gousheh, S., Abdolmaleki, A., Nikkhah Dehnavi, A., & Shahrashoob, A. (2025). AI-driven control algorithm using machine learning and genetic optimization for enhancing visual comfort in adaptive façades. Automation in Construction, 179, Article 106474. https://doi.org/10.1016/j.autcon.2025.106474.
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Abstract
Effective management of daylight and visual comfort in office spaces remains a challenge, as existing shading systems often lack adaptability to changing environmental conditions and occupant needs. This paper presents an AI-driven real-time shading control algorithm that optimizes visual comfort using machine learning-based surrogate models and evolutionary optimization. A non-conventional adaptive façade was simulated using Radiance and Ladybug Tools across nine U.S. climates. Four machine learning models were evaluated for predicting Task Illuminance (Et) and Vertical Eye Illuminance (Ev), with Extra Trees achieving the highest accuracy (R2
= 0.95). A Non-dominated Sorting Genetic Algorithm II (NSGA-II) balances glare reduction and daylight utilization by optimizing façade configurations in real time. In contrast to prior approaches constrained to fixed geometries and single-objective control, this paper introduces a generalizable multi-objective control framework. Results show that AI-driven optimization significantly improves adaptive façade performance, offering a scalable solution for intelligent daylight and comfort management.
Keywords
Smart façade control; Machine learning; Surrogate models; Visual comfort; Task illuminance; Vertical eye illuminance; Dynamic shading
Akerele, D. D., & Aguayo, F. (2025). A Comparative Evaluation of Polymer-Modified Rapid-Set Calcium Sulfoaluminate Concrete: Bridging the Gap Between Laboratory Shrinkage and the Field Strain Performance. Buildings (Basel), 15(15), 2759. https://doi.org/10.3390/buildings15152759.
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Abstract
Rapid pavement repair demands materials that combine accelerated strength gains, dimensional stability, long-term durability, and sustainability. However, finding materials or formulations that offer these balances remains a critical challenge. This study systematically evaluates two polymer-modified belitic calcium sulfoaluminate (CSA) concretes—CSAP (powdered polymer) and CSA-LLP (liquid polymer admixture)—against a traditional Type III Portland cement (OPC) control under both laboratory and realistic outdoor conditions. Laboratory specimens were tested for fresh properties, early-age and later-age compressive, flexural, and splitting tensile strengths, as well as drying shrinkage according to ASTM standards. Outdoor 5 × 4 × 12-inch slabs mimicking typical jointed plain concrete panels (JPCPs), instrumented with vibrating wire strain gauges and thermocouples, recorded the strain and temperature at 5 min intervals over 16 weeks, with 24 h wet-burlap curing to replicate field practices. Laboratory findings show that CSA mixes exceeded 3200 psi of compressive strength at 4 h, but cold outdoor casting (~48 °F) delayed the early-age strength development. The CSA-LLP exhibited the lowest drying shrinkage (0.036% at 16 weeks), and outdoor CSA slabs captured the initial ettringite-driven expansion, resulting in a net expansion (+200 µε) rather than contraction. Approximately 80% of the total strain evolved within the first 48 h, driven by autogenous and plastic effects. CSA mixes generated lower peak internal temperatures and reduced thermal strain amplitudes compared to the OPC, improving dimensional stability and mitigating restraint-induced cracking. These results underscore the necessity of field validation for shrinkage compensation mechanisms and highlight the critical roles of the polymer type and curing protocol in optimizing CSA-based repairs for durable, low-carbon pavement rehabilitation.
Keywords
calcium sulfoaluminate cement (CSA); polymer-modified confrete (PMC); rapid-set concrete; early-age shrinkage; temperature-induced strain; outdoor vs. laboratory performance; sustainable concrete; field performance; mechanical properties
Prof. Dossick’s CM515 Spring 2025 Class. Eds. Dossick, Carrie. & Ray, Lauren. (2025). 2025 Innovation in the Construction Industry. University of Washington. Pressbooks. Seattle, WA. https://uw.pressbooks.pub/2025innovationcm/
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- Contributors
- Abhishek Tripathy; Ashutoshsinh Shekhawat; Harshitha P S; Adam Hinds; James Mirie; John Ales; Shraddha Kalyani; Muhammad Abu Bakar Tariq; Mohamed Ibrahim; Mikhail Kamila; Manshuk Sabyrova; Nicholas Miranda; Nisha Tomar; Omid Keivanloo; Rahul Varma Alluri; Idris Soliu; Vidheesha Kasthoori Channapatnam Badrinath; Monica Korlepara; Vakkachan Johny Manipadam; Swetha Suresh; Vinay Singh; Vy Le; William Ackerman; and Yingjie Liu
Abstract
A collection of case studies about adopting new technologies and changing practices in design, construction, and operations of the built environment.
Keywords
Technology; Engineering; Agriculture; Industrial Processes
Akerele, D. D., Aguayo, F., & Wu, L. (2025). Portland Limestone Cement in Concrete Pavement and Bridge Decks: Performance Evaluation and Future Directions. Buildings, 15(5), 660. https://doi.org/10.3390/buildings15050660
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Abstract
With the rising demand for sustainable infrastructure, addressing the limitations of Ordinary Portland Cement (OPC) is crucial, particularly for exposed structures such as pavements and bridge decks. Portland limestone cement (PLC) is a sustainable alternative that delivers environmental benefits and comparable performance. This study used a systematic review and meta-analysis with a random-effects model to evaluate PLC’s strength development, durability, and sustainability. The findings indicate that PLC generally matches or surpasses OPC in terms of compressive strength, freeze–thaw resistance, and sulfate durability. However, its setting time and early-age cracking require further optimization, especially in cold climates. Additionally, this study highlights the fire performance advantages of PLC and its enhanced chloride resistance. The analysis identified critical research gaps, including long-term field performance and regional adaptation to extreme environmental conditions. These findings contribute to a deeper understanding of PLC’s role in sustainable construction and offer future research directions on hybrid cements and admixture compatibility.
Keywords
Portland limestone cement (PLC); type 1L cement; concrete pavement; bridge deck; sustainability; low-carbon; CO2 reduction
Dr. Fred Aguayo, Assistant Professor in the Department of Construction Management, has been awarded a Royalty Research Fund for his project entitled “Developing a Reliable, Rapid, and Robust (R3) Performance Test Method for Carbonation Induced Corrosion of Cement-Based Mixtures.” Congratulations to Dr. Aguayo!
Xiao, B., Wang, Y., Zhang, Y., Chen, C., & Darko, A. (2024). Automated daily report generation from construction videos using ChatGPT and computer vision. Automation in Construction, 168, 105874-. https://doi.org/10.1016/j.autcon.2024.105874
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Abstract
Daily reports are important in construction management, informing project teams about status, enabling timely resolutions of delays and budget issues, and serving as official records for disputes and litigation. However, current practices are manual and time-consuming, requiring engineers to physically visit sites for observations. To fill this gap, this paper proposes an automated framework to generate daily construction reports from on-site videos by integrating ChatGPT and computer vision (CV)-based methods. The framework utilizes CV methods to analyze video footage and extract relevant productivity and activity information, which is then fed into ChatGPT using proper prompts to generate daily reports. A web application is developed to implement and validate the framework on a real construction site in Hong Kong, generating daily reports over a month. This research enhances construction management by significantly reducing documentation efforts through generative artificial intelligence, with potential applications in jobsite safety management, quality reporting, and stakeholder communication.
Keywords
Construction daily report generation; Computer vision; ChatGPT; Construction management; Project documentation
Professor Carrie Sturts Dossick, Associate Dean for Research, and Assistant Professor Lingzi Wu both from the department of Construction Management, presented at the 2024 Northwest Construction Consumer Council (NWCCC) Conference, “AI and Digital Technology in Construction” and Distinguished Project Awards. Their presentations are linked below. Assistant Professor Wu gave a presentation entitled “AI-Powered Solutions for Next-Generation Construction Management.” Professor Sturts Dossick presented on Cybersecurity Planning.
Kim, M., & Kim, Y.-W. (2024). Applications of blockchain for construction project procurement. Automation in Construction, 165, 105550-. https://doi.org/10.1016/j.autcon.2024.105550
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Abstract
Blockchain technology has shown potential in enhancing project performance by instilling trust in data sharing among stakeholders, thereby encouraging the stakeholders to ensure a strategic acquisition and resource management through procurement activities. However, despite the recent research efforts on blockchain in the construction sector, there is a lack of knowledge of the status quo in that barely any research investigated the synergy of blockchain and procurement by recognizing the inextricable linkage between procurement management and project delivery system. This paper conducts a systematic review of 54 articles to assess blockchain's potential in addressing issues inherent in the current organizational structures and collaborative efforts. Findings offer profound insight into the current landscape of procurement-specific blockchain research, highlighting areas needing attention. This paper identified opportunities in construction procurement by investigating the extent to which the technology is integrated into the current project management context emphasizing integration and collaboration.
Keywords
Blockchain; Procurement; Construction industry; Procurement process; Project delivery system; Literature review
Tabatabaei Manesh, M., Nikkhah Dehnavi, A., Tahsildoost, M., & Alambeigi, P. (2024). Acoustic design evaluation in educational buildings using artificial intelligence. Building and Environment, 261, 111695-. https://doi.org/10.1016/j.buildenv.2024.111695
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Abstract
Speech intelligibility is a critical aspect of building science, particularly in educational buildings where poor sound quality may have a detrimental impact on students' learning and teachers’ health. However, considering the numerous building regulations proposing varying definitions and ranges of acoustic comfort, calculating the necessary acoustic indicators can be challenging for designers. Speech intelligibility is a crucial component of indoor acoustics and acoustic comfort and can be calculated using formulas, simulation software, and data-based web tools. While formulas are fast, they lack details; acoustic simulation software is highly accurate but time-consuming and expensive. Data-based web tools, including machine learning algorithms, offer both speed and accuracy and are widely accessible. In this study, we present a system utilizing machine learning techniques to predict acoustic indicators, numeric and heatmap, in an educational building. The Pachyderm plugin in the Grasshopper was utilized to conduct extensive simulations in a single educational space, focusing on acoustic indicators in six different frequencies and general modes. Using Catboost and the pix2pix algorithm, the prediction models provide numerical and image indices on the developed dataset. Also, SHAP values were employed to interpret the Catboost model, analyzing the significance of each feature. The results showed remarkable accuracy, (i.e., between 89 % and 99 %) in the numerical portion, and PSNR index ranging from 0.817 to 0.970, and an SSIM index ranging from 15.56 to 31.57 in the image section. By utilizing data-driven methods, the system provides an efficient and accurate approach to calculating acoustic indicators, helping to ensure optimal acoustic environment in educational buildings.
Keywords
Building acoustics; Catboost; Pix2pix; Educational building; Speech intelligibility