Reza Maalek

The construction industry continues to face challenges in adopting advanced technologies, such as construction robotics, from research to practice. From a lean management perspective, this constitutes wasted scientific effort and limited gain in efficiency and productivity of construction processes. Traditionally, …

This volume comprises selected peer-reviewed proceedings from the CSCE Winnipeg 2025 specialty conference focusing on sustainable development. Contributions range from fundamental research to pract...

Background: The use of point clouds to perform quality control (QC) of prefabricated structural systems is growing markedly. The sustained application of point clouds on construction projects at scale, however, requires reliable and automated frameworks to foster practicality. Literature Review: Existing solutions seldom incorporate the impact of instrument artifacts, construction errors, and complex element geometries, rendering the frameworks error-prone and sensitive to thresholds, outliers, and missing data. Current methods suffer from the following: (i) are not generalizable to any freeform element; (ii) rely on local primitives, which increases runtime and threshold subjectivity; (iii) do not formulate the point cloud to model fitting based on geometric distance; and (iv) do not incorporate a dedicated outlier detection to explicitly handle large outliers. Methodology: This study presents, scan-to-QC, a generic solution for automatic QC of prefabricated elements with non-analytic cross-sections using point clouds. The framework addresses the current state-or-the-art limitations (above) by providing a generic, direct artificial-intelligence (AI)-based geometrid distance fitting robust to outliers. The proposed approach introduces four new algorithms to: (i) represent the cross-sectional boundaries of elements with trigonometric polynomials, (ii) perform orthogonal fitting of points to these polynomials, (iii) remove outlying observations through robust concentration steps (CS), and (iv) perform assembly verification of prefabricated elements in construction projects. As the convergence of CS depends on the initial fitting parameters, the performances of seven deterministic, heuristic, and AI-based (i.e., metaheuristic and global computational intelligence-based) optimization strategies to recover these parameters were evaluated on point clouds of five common prefabricated elements with complex geometries. The comparative analysis considered factors such as the fit quality, object detection accuracy, and computation runtime. Results and Conclusions: Among these methods, the Particle Swarm (PS) achieved the best results, significantly outperforming Simulated Annealing, Bayesian, and Genetic Algorithm. Furthermore, by fine-tuning the swarm size, the PS attained 99% and 96% accuracies in object detection and fitting quality, respectively. Finally, the calibrated PS algorithm was applied to the point cloud of a real-world prefabricated spaceframe structure, yielding 98.5% reliability in nodal coordinate estimation compared to the ground truth.

The problem of minimizing the cost and time of rework due to fabrication and assembly errors in prefabricated construction considering the spatial relationship between elements was revisited. The time required to remedy the element's error was used as the basis to find optimal crew sizing and scheduling to minimize rework. Using the modeled time, two formulations, namely, a mixed integer linear programming (MILP) and mixed integer quadratically constrained programming (MIQCP) were derived. The latter formulation captured the impact of crew occupied space on the available space for adjacent crews. Furthermore, the impact of uncertainty in the estimated time demand for each zone was incorporated through robust optimization. Finally, a new objective function term was included to balance work demand in each zone to approach takt time scheduling. The formulations were applied on a typical building construction project to correct the locations of reinforced concrete columns with assembly errors. While the formulations provide direct crew sizing and scheduling results, the methodology can benefit from: (i) incorporating flow continuity within zones; (ii) including multiple objectives to capture aspects, such as sustainability; and (iii) estimating the terms of the time demand model from real data.

Providing safe, environmentally conscious, and cost-effective designs is the primary duty of civil engineers. To this end, many different algorithms and methods have been developed in parallel with the progress of digital technologies over the past decades. Techniques such as AI-based Metaheuristic Algorithms (MAs), Reliability Analysis, and Building Information Modelling (BIM) are some of those methods that serve this purpose. The present study focuses on establishing a design optimization methodology by implementing the techniques in the open literature on one software environment to create a robust engineering and architectural workflow. The methodology involves multiple stages such as (i) creating parametric trusses employing Visual Programming (VP) software Dynamo (Version: 3.0.4); (ii) performing a First-order Reliability Method (FORM) analysis which includes a Finite Element Method (FEM) analysis as a Limit State Function (LSF); (iii) employing MAs to achieve optimum design variables under uncertain design constraints; (iv) testing the methodology with various real-word examples and scenarios; (v) creating an optimized model on Robot Structural Analysis 2024 (RSA) software in real time in the purpose of further adjustments. The results demonstrated that creating a design optimization workflow by utilizing a BIM environment can enhance the design process by easing the storing, sharing, and utilizing of design data by different branches capable of performing different complicated tasks successfully.

This study proposes incorporating generative artificial intelligence large language models (LLMs) into the Master of Science (M.Sc.) curriculum on digitization in construction. The aim was to help students generate computer code to solve, automate, and streamline practical challenges in advanced construction engineering and management (CEM). To this end, a host of problem-based learning (PBL) individual assignments and collaborative team projects were developed, alongside a combination of flipped classroom models and blended learning lessons, in order to teach effective interactions with LLMs and mitigate concerns, such as bias and hallucination. The effective interaction with LLMs not only facilitated code generation, which would otherwise be complex without additional formal training, but also provided a platform for strengthening basic project management skills, such as departmentalization, work breakdown structuring, modularization, activity delegation, and defining key performance indicators. The effectiveness of this approach was quantitatively and qualitatively evaluated within two new modules, Digital Engineering and Construction and Digital Technologies in Field Information Modeling. These modules were offered over three semesters each as part of a new M.Sc. program in Technology and Management in Construction at the Karlsruhe Institute of Technology. It was observed that 86.4% of students fully completed the PBL projects, while the remaining 13.6% achieved over 50% completion across all six semesters. Furthermore, anonymous student surveys indicated a teaching quality index of 100% in five semesters and 96.4% in one semester. These preliminary results suggest that the proposed strategy can be used to effectively integrate LLMs to support students in code generation for open-ended projects in CEM. Further research was, however, found to be necessary to ensure the sustainable revision and redesign of the problems as LLM capabilities evolve.

The optimal structural design is imperative in order to minimize material consumption and reduce the environmental impacts of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as the genetic algorithm (GA). However, the integration of AI-based optimization, together with visual programming (VP), in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps, including the following: (i) generation of parametric trusses in Dynamo VP; (ii) performing finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. This methodology provides a new interoperable framework with minimal interference with existing supply-chain processes, and it will be flexible to technology literacy and allow architectural, engineering and construction (AEC) professionals to employ VP, global optimization, and FEM in BIM-based projects by leveraging open-sourced software and tools, together with commonly used design software. The feasibility of the proposed workflow was tested on benchmark problems and compared with the open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction. Despite the success of this study in developing a workable, user-friendly, and interoperable framework for the utilization of VP, GA, FEM, and BIM for structural optimization, the results obtained could be improved by (i) increasing the callback function speed between Dynamo and RSA through specialized application programming interface (API); and (ii) fine-tuning the GA parameters or utilizing other advanced global optimization and supervised learning techniques for the optimization.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.