Machine Learning-Based Model for Predicting the Shear Strength of Slender Reinforced Concrete Beams without Stirrups

Alshboul, Odey; Almasabha, Ghassan; Shehadeh, Ali; Mamlook, Rabia Emhamed Al; Almuflih, Ali Saeed; Almakayeel, Naif

doi:10.3390/buildings12081166

Cited by 29 publications

(14 citation statements)

References 52 publications

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“…Gene expression (GEP) algorithms can also be implemented to predict the efficiencies of the construction workers. GEP produces better results than regression models (Alshboul et al, 2022a). This study can also be implemented to form and optimize the construction of green buildings.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Simplified Solution of Time-Cost Trade-off Problem for Building Constructions by Linear Scheduling

Bettemir¹

2023

JJCE

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Time-cost trade-off is an important optimization problem for contractors, because its optimum solution minimizes the total project cost. Formation of the time-cost trade-off problem causes an important workload, since the problem requires preparation of different construction cost and duration alternatives of the activities. Previous studies focused on the optimum solution of the problem and ignored the difficulties of the preparation of the different construction alternatives for the activities, which is a very difficult task for contractors. In this study, the creation of construction alternatives consisting of different time and cost values is automated. Quantity take-off of construction items is computed by user-defined dimensions of structural elements. Workmanship and material requirements are computed by pre-defined job descriptions and quantity take-off values. Different construction alternatives are formed by assigning different crew sizes and the corresponding construction durations are computed by estimating the job efficiency of the crew by regression models derived from the literature. Precedence relationships of the main construction items are pre-defined and the construction schedule is formed by a line of balance in terms of work days. The problem is optimized by a genetic algorithm the parameters of which are fine-tuned by experimental design. The developed approach is implemented on a spreadsheet application and the total optimization process including data entry is completed in one and a half hour on a desktop computer with i5 CPU. This study contributes to relevant literature by proposing a systematic approach for the formation of construction alternatives of the time-cost trade-off problem. The proposed approach can be beneficial for contractors and project managers to form and solve the time-cost trade-off problem with minimum endeavor and cost. KEYWORDS: Time-cost trade-off, Genetic algorithm, Line of balance, Optimization, Experimental design.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Simplified Solution of Time-Cost Trade-off Problem for Building Constructions by Linear Scheduling

Bettemir¹

2023

JJCE

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“…Alshboul et al [54] used a hybrid mathematical and machine learning prediction approach to evaluate the impact of external support on green building construction costs. In another study, Alshboul et al [55] used a machine learning-based model to predict the shear strength of slender reinforced concrete beams. Aslam et al [56] used hybrid machine learning and data mining algorithms for water quality management.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

et al. 2023

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Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system.

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“…Furthermore, a conceptual architecture is proposed, mapping the tools materializing cognition within the DT core, along with a cognitive process that enables resilience in production through the utilization of CDTs. Papers [26][27][28][29][30][31][32][33][34][35][36][37][38][39] are included in this analysis to illustrate that the CDT concept is utilized in various industrial domains, such as process industry [28], manufacturing [29][30][31], maintenance management [32], construction [33][34][35][36][37] where works [36,37] propose ML models suitable for the design of DTs in the construction industry, and health care [38,39]. Additionally, publication [40] with its thematic focus on DTs in learning and education attracted the attention of the authors of this paper.…”

Section: Related Workmentioning

confidence: 99%

Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

Savić,

Segedinac,

Konjović

et al. 2023

Sustainability

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In this paper, we propose an abstract domain-neutral architecture for a cognitive digital twin (CDT) and a software platform to develop such CDTs, including machine reasoning capabilities. Sustainable development refers here to an abstract model that enables a holistic view of limiting resources and has an ability to adapt to different application domains while reusing existing resources. The proposed solution allows for a unified abstract representation and the development of a wide range of diverse digital twins, as well as facilitating their interoperability. The abstract architecture consists of a four-layer structure (observation/actuation layer, data management layer, reasoning layer, and simulation layer) with an upper ontology to which the domain ontology of the specific CDT is mapped. The architecture relies on semantic web technologies, including ontology-based reasoning using OWL, and a loosely coupled, component-based service-oriented software architecture. The platform utilizes a microservice architecture that enables separate, loosely coupled services on each layer, message queues to provide asynchronous communication, and possesses cloud technologies to achieve scalability. The proposed approach was validated by implementing a software platform prototype and demonstrating its key features through two dissimilar scenarios. The first scenario demonstrates simple sustainable energy management through IoT systems inside smart buildings, while the second one demonstrates knowledge quality management based on knowledge space theory.

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Machine Learning-Based Model for Predicting the Shear Strength of Slender Reinforced Concrete Beams without Stirrups

Cited by 29 publications

References 52 publications

Simplified Solution of Time-Cost Trade-off Problem for Building Constructions by Linear Scheduling

Simplified Solution of Time-Cost Trade-off Problem for Building Constructions by Linear Scheduling

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

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