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In response to the critical need for enhanced resource management in the construction industry, this research develops an innovative, integrated methodology that synergistically combines Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies. Central to our approach is a sophisticated technological framework that incorporates a Client Early Warning System (CEWS) and a Decision Support System (DSS). These systems facilitate real-time monitoring and management of construction resources, ensuring operational efficiency and optimal resource utilization. Our methodology was empirically validated through a comprehensive case study at Helwan University's College of Engineering. The results demonstrated a significant enhancement in operational efficiency, particularly in resource allocation and progress tracking. Key practical outcomes include the development of a CEWS master dashboard that provides in-depth, real-time insights into project metrics. This dashboard was crucial for managing compliance with health protocols during the COVID-19 pandemic, showcasing the framework's adaptability to critical health standards. Further, the integration of indoor tracking technology revolutionized attendance tracking by replacing outdated manual methods with automated processes. This capability not only underscores the practical applicability of our research but also establishes a new benchmark for future technological advancements in construction project management. Our study sets the stage for subsequent innovations, paving the way for a more connected, efficient, and data-driven approach in the construction industry. [ABSTRACT FROM AUTHOR]

Fly ash-based geopolymer concrete is studied in this research work for its compressive strength, life cycle and environmental impact assessment contribution to the construction environment. This is in line with the United Nations’ sustainable development goals SDG9 and SDG11. However, the focus of this research paper is on the sustainability of geopolymer concrete and its overall environmental impact. The metaheuristic machine learning approaches have been deployed to predict the compressive strength (CS) of the GPC based on environmental impact considerations of the concrete constituent materials, which included fly ash, sodium silicate, sodium hydroxide, fine and coarse aggregates. The metaheuristic techniques include the k-Nearest Neighbour (kNN), support vector regression (SVR), and random forest regression (RFR), where all are optimized with the particle swarm (PSO). These metaheuristic techniques have been modified for this research work with new codes to enhance innovation in terms of run time and efficiency. The results of the life cycle assessment (LCA) evaluation of the GPC mixes based on the Ecoinvent 3 available in SimaPro and Eco-indicator 99 and CML 2001 modified in the framework of ReCiPe 2016 recent development show reduced potential of environmental acidification due to increased fly ash (FA) in the GPC mixes compared to previous results. The decisive CS and LCA predictive models, RFR-PSO and SVR-PSO respectively performed optimally above 90% and better than previous models from the literature. Overall, they present an innovative metaheuristic smart technology for the prediction of the GPC infrastructure behavior and performance integrity.

This paper provides a comprehensive review of the current state of 3D printing technology in the construction industry, highlighting the potential applications, benefits, and future directions of this emerging field. The review indicates that 3D printing technology has the potential to revolutionize the construction industry by offering more efficient, precise, and sustainable methods of construction. The technology offers numerous advantages, including the ability to create complex geometries and custom components, improved precision and accuracy, reduction in waste materials, improved worker safety, and potential for use in remote or inaccessible locations. Furthermore, the advent of additive manufacturing, colloquially known as 3D printing, presents prospects for the advancement of novel material compositions, printing methodologies, and cybernetic systems that have the potential to optimize the efficiency and effectiveness of the construction domain. Future research should focus on developing larger printers with more efficient support structures, improving the accuracy and speed of printing, and exploring the potential of using new and innovative materials in the construction process. Additionally, the environmental impact of 3D printing technology should be further examined, particularly in terms of its potential for reducing waste and energy consumption in the construction industry. Overall, the potential utilizations and advantageous outcomes stemming from the implementation of 3D printing technology within the construction sector are momentous. Persistent exploration and innovation within this realm hold the capacity to engender noteworthy strides in construction technology and foster heightened sustainability within building methodologies.

The flow of Bingham non-Newtonian incompressible fluids like concrete is associated with the large deformation of materials. The modeling and simulation of these fluids’ flow behavior by using conventional numerical methods. suffer problem-formulation setbacks due to mesh distortion. In order to compensate for the mathematical inefficiencies encountered in the process, particle-based methods have evolved and been applied. Also, the use of some particle-based methods produces a stretch of unreliability due to the Eulerian algorithmic trail, which visits every particle edge allowing for revisiting vertices during its operation. This makes the model path cumbersome and time-consuming. Concrete flow is an important element of sustainable infrastructural development, and its understanding strengthens the efficiency of concrete handling and placement during construction activities. In this paper, a mesh-free method of modeling the flowability of self-compacting concrete (SCC) known as the smoothed particle hydrodynamics (SPH) has been reviewed. It derives its advantage from the Lagrangian algorithmic trail. This explores its merits and demerits in the concrete construction industry to propose the best practices for the passing ability, filling ability, and dynamic stability of the flowing fresh concrete (FFC)

In this paper, selected materials that influence the viscosity of the self-consolidating concrete (SCC) are introduced like the Limestone Powder (LSP), the High Range Water Reducing Admixture (HRWRA), which reduce the interparticle force between concrete constituents like the aggregates, and other superplasticizers. Moreover, in serious attempts to design the SCC for different infrastructure requirements, there have been repeated laboratory visits, which need to be reduced. In this research paper, the artificial intelligence (AI) methods: Artificial Neural Network (ANN), Evolutionary Polynomial Regression (EPR), and Genetic programming (GP) have been deployed to predict the slump flow (SF), V-funnel flow time (VFFT), L-box ratio (LBR) or passing ratio, and Orimet flow time (OFT) of LSP-admixed SCC. The independent variables of the predictive model were cement, LSP, water, water-binder ratio, HRWRA, sand, and coarse aggregates of 4/8 mm and 8/16 mm sizes. The flow tests were conducted after 5 minutes of waiting time after mixing. The model results showed ANN with superior intelligent learning ability over previous models in terms of overall performance.

The effect of Pond Ash (PA) activated with sodium chloride (NaCl) solution and reinforced with glass powder on the mechanical properties of soft clay soil, which comprise of the California bearing ratio (CBR) and the unconfined compressive strength (UCS) has been studied in this research work. The PA requires pozzolanic improvements to meet the ASTM C618 requirements for pozzolanas. In the present research paper, further emphasis has been on the machine learning prediction of CBR and UCS of the soft clay soil stabilized with a composite of PA. Generally, the studied soft clay soil properties, which were the microstructure, microspecter/micrograph, oxide composition, Atterberg limits, compaction behavior, free swell index (FSI), CBR and UCS significantly improved due to the enhanced cementitious ability of the activated and reinforced PA. The multiple data collected from this general stabilization result were used to predict the soil’s CBR and UCS by the artificial neural network (ANN) technique. The results showed high performance of the model in terms of the sum of squares error (SSE) of 1.5% and 2.0% and the coefficient of determination (R2) of 0.9979 and 0.9973 for the CBR and UCS models, respectively. The models also outclassed the performances of other models from the literature.