Borja Garcia de Soto

Pi Ko, Samuel A. Prieto, Borja Garcia de Soto

Inspection of cracks is an important process for properly monitoring and maintaining a building. However, manual crack inspection is time-consuming, inconsistent, and dangerous (e.g., in tall buildings). Due to the development of open-source AI technologies, the increase in available Unmanned Aerial Vehicles (UAVs) and the availability of smartphone cameras, it has become possible to automate the building crack inspection process. This study presents the development of an easy-to-use, free and open-source Automated Building Exterior Crack Inspection Software (ABECIS) for construction and facility managers, using state-of-the-art segmentation algorithms to identify concrete cracks and generate a quantitative and qualitative report. ABECIS was tested using images collected from a UAV and smartphone cameras in real-world conditions and a controlled laboratory environment. From the raw output of the algorithm, the median Intersection over Unions for the test experiments is (1) 0.686 for indoor crack detection experiment in a controlled lab environment using a commercial drone, (2) 0.186 for indoor crack detection at a construction site using a smartphone and (3) 0.958 for outdoor crack detection on university campus using a commercial drone. These IoU results can be improved significantly to over 0.8 when a human operator selectively removes the false positives. In general, ABECIS performs best for outdoor drone images, and combining the algorithm predictions with human verification/intervention offers very accurate crack detection results. The software is available publicly and can be downloaded for out-of-the-box use at: https://github.com/SMART-NYUAD/ABECIS

Bilal Ahmed, Yuqing Qiu, Diab W. Abueidda et al.

Finite element modeling is a well-established tool for structural analysis, yet modeling complex structures often requires extensive pre-processing, significant analysis effort, and considerable time. This study addresses this challenge by introducing an innovative method for real-time prediction of structural static responses using DeepOnet which relies on a novel approach to physics-informed networks driven by structural balance laws. This approach offers the flexibility to accurately predict responses under various load classes and magnitudes. The trained DeepONet can generate solutions for the entire domain, within a fraction of a second. This capability effectively eliminates the need for extensive remodeling and analysis typically required for each new case in FE modeling. We apply the proposed method to two structures: a simple 2D beam structure and a comprehensive 3D model of a real bridge. To predict multiple variables with DeepONet, we utilize two strategies: a split branch/trunk and multiple DeepONets combined into a single DeepONet. In addition to data-driven training, we introduce a novel physics-informed training approaches. This method leverages structural stiffness matrices to enforce fundamental equilibrium and energy conservation principles, resulting in two novel physics-informed loss functions: energy conservation and static equilibrium using the Schur complement. We use various combinations of loss functions to achieve an error rate of less than 5% with significantly reduced training time. This study shows that DeepONet, enhanced with hybrid loss functions, can accurately and efficiently predict displacements and rotations at each mesh point, with reduced training time.

Eyob Mengiste, Borja Garcia de Soto, Timo Hartmann

This study proposes a method to automate the development of lookahead planning. The proposed method uses construction material conditions (i.e., appearances) and site space utilization to predict task completion rates. A Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) model was trained using a segment of a construction project timeline to estimate completion rates of tasks and propose data-aware lookahead plans. The proposed method was evaluated in a sample construction project involving finishing works such as plastering, painting, and installing electrical fixtures. The results show that the proposed method can assist with developing automated lookahead plans. In doing so, this study links construction planning with actual events at the construction site. It extends the traditional scheduling techniques and integrates a broader spectrum of site spatial constraints into lookahead planning.

Hazem Ibrahim, Fengyuan Liu, Rohail Asim et al.

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work -- a possibility that has sparked discussions on the integrity of student evaluations in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses. Further, students' perspectives regarding the use of such tools, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of ChatGPT against students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use. We find that ChatGPT's performance is comparable, if not superior, to that of students in many courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection. Finally, we find an emerging consensus among students to use the tool, and among educators to treat this as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of AI into educational frameworks.