This paper studied the effect of fly ash (FA) and natural pozzolana (NP) as partial cement substitutes on sustainable concrete permeability and mechanical and microstructural properties. Batches with 10, 20, 30, 40, and 50% FA and NP replacements for cement were prepared and tested for compressive strength after 7, 28, and 56 curing days and for flexural strength after 28 curing days. Permeability testing was conducted on all samples. A qualitative microstructural analysis was performed using scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX). The mechanical properties results showed slight strength improvements when replacing the cement with low percentages of the pozzolanic materials. The compressive strengths of the batches with 10% FA and NP replacements of cement showed compressive strength increases of 11.63 and 8.75%, respectively, compared to that of plain concrete. On the other hand, the flexural strength for the batches with FA replacement of cement achieved at least a 15.6% increase compared to that of the control. Moreover, FA and NP replacement of cement had a positive impact on batch permeability, with decreased permeability values reaching 78.3 and 56.5%, respectively, compared to that of the control. Furthermore, the microstructural analysis indicated that adding FA and NP would enhance cement hydration by the formation of dense hydration products such as calcium–silicate–hydrate (C-S-H) crystals, which increased hardened concrete strength. Finally, the cost analysis showed that the batch with a 10% FA replacement of cement is the ideal one in this study.

The limited availability of reconstruction resources is one of the main challenges that often confront postdisaster recovery of damaged transportation networks. This requires an effective and efficient deployment and utilization of these limited resources in order to minimize both the performance loss of the damaged transportation network and the reconstruction costs. This paper presents the development of a robust model for planning postdisaster reconstruction efforts that is capable of: (1) optimizing the allocation of limited reconstruction resources to competing recovery projects; (2) assessing and quantifying the overall functional loss of damaged transportation networks during the recovery efforts; (3) evaluating the impact of limited availability of resources on the reconstruction costs; and (4) minimizing the performance loss of transportation networks and reconstruction costs. The model utilizes the user equilibrium algorithm to enable the assessment of the transportation network performance loss...

This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as ChatGPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family residential project and an office building project demonstrated the system’s ability to reduce the time and effort required for compliance checks while improving accuracy. It streamlined the identification of violations, such as non-compliant room dimensions, material usage, and object placements, by automatically assessing relationships and generating actionable reports. Compared to manual methods, the system eliminated repetitive tasks, simplified complex regulations, and ensured reliable adherence to standards. By offering a comprehensive, adaptable, and cost-effective solution, this proposed approach offers a promising advancement in BIM-based compliance checking, with potential applications across diverse regulatory documents in construction projects.

Postdisaster recovery efforts of damaged civil infrastructure systems need to be optimized in order to alleviate the adverse impacts of natural disasters on local societies and economies. This paper presents an innovative framework that integrates two newly developed models for resource utilization and multiobjective optimization that are designed to optimize these recovery efforts. The developed models provide new and unique capabilities, including (1) allocating limited reconstruction resources to competing recovery projects, (2) estimating the reconstruction duration and cost associated with implementing specific recovery plans, and (3) generating optimal trade-offs between minimizing the reconstruction duration and cost. An application example is analyzed to evaluate the performance of the developed models and demonstrate their capabilities in identifying a wide spectrum of optimal reconstruction plans, where each provides a unique and nondominated trade-off between minimizing the recovery duration an...

Because of their potential danger to public health, economic loss, environmental damage, and energy waste, underground water pipelines leaks have received more attention globally. Researchers have proposed active leakage control approaches to localize, locate, and pinpoint leaks. Noise loggers have usually been used only for localizing leaks while other tools were used for locating and pinpointing. These approaches have resulted in additional cost and time. Thus, regression and artificial neural network (ANN) models were developed in this study to localize and locate leaks in water pipelines using noise loggers. Several lab experiments have been conducted to simulate actual leaks in a sample ductile iron pipeline distribution network with valves. The noise loggers were used to detect these leaks and record their noise readings. The recorded noise readings were then used as input data for the developed models. The ANN models outperformed regression models during testing. Moreover, ANN models were s...

This paper presents an overview of the neural-network technique as a management tool for maintenance of flexible pavement. The paper discusses the development and implementation of a neural network for the condition rating of roadway sections. The condition-rating scheme developed by Oregon State Department of Transportation was used as the basis for the development of the network presented. A training set of 744 cases was used to train the network, and a set of 1736 cases was used to test the generalization ability of the system. The network adequately learned the training examples with an average training error of 0.019 and was able to determine the correct condition ratings with an average testing error of 0.023. The network's ability to deal with noisy data also was tested. Up to 60% noise was added to the data and introduced to the network. The results showed that the network presented could identify condition rating relationships at high levels of-noise. Finally, an expert determination was compared with that produced by the network. The network was able to mimic the expert's condition ratings with an average error of 0.0354.