Evaluating the Impact of External Support on Green Building Construction Cost: A Hybrid Mathematical and Machine Learning Prediction Approach

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

doi:10.3390/buildings12081256

Cited by 36 publications

(16 citation statements)

References 70 publications

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“…This study can also be implemented to form and optimize the construction of green buildings. The cost of green buildings can be estimated by machine learning (Alshboul et al, 2022b) LoB represents the construction schedule with less number of activities, so that the constraints between the activities are relatively easy to define. This approach enables the utilization of effective construction scheduling methods which help reduce the total construction cost (Wong and Mohammed Ahmed, 2018).…”

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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“…The main conclusions are as follows: (1) Ghost convolution is used instead of traditional convolution in the backbone network to form a new network; Ghost ResNet greatly reduces the number of parameters of the model and improves the detection efficiency at the expense of detection accuracy. (2) The proposed channel aDention module that fuses width and height information can beDer capture the spatial information of important regions in the image, which is beneficial to accurately locating target locations and improves detection accuracy while adding only a small number of network parameters; (3) CARAFE up-sampling is used to replace the DCN module and generate different up-sampling kernels for different locations during the up-sampling process to fully integrate feature map information and enhance the feature extraction capability of the network. Compared with the DCN module, CARAFE up-sampling introduces fewer parameters and has a lower network burden.…”

Section: Discussionmentioning

confidence: 99%

“…As an active microwave sensing technology, synthetic aperture radar (SAR) has the characteristics of being independent of light and weather conditions and having strong penetration and all-weather detection capabilities. With the development of SAR imaging technology, SAR images have been widely used in military and civilian fields [1,2]. Building target detection in SAR images can quickly obtain building area information, which has important research significance in urban construction planning, military reconnaissance, disaster assessments, target strikes, etc.…”

Section: Introductionmentioning

confidence: 99%

Application of Minnan Folk Light and Shadow Animation in Built Environment in Object Detection Algorithm

Wu¹,

Huang²,

Xiong

et al. 2023

Buildings

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To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is proposed. Firstly, based on the rotating target detection algorithm R-centernet, the Ghost ResNet network is constructed to reduce the number of model parameters by replacing the traditional convolution in the backbone network with Ghost convolution. Secondly, a channel attention module integrating width and height information is proposed to enhance the network’s ability to accurately locate salient regions in folk light images. Content-aware reassembly of features (CARAFE) up-sampling is used to replace the deconvolution module in the network to fully incorporate feature map information during up-sampling to improve target detection. Finally, the constructed dataset of rotated and annotated light and shadow SAR images is trained and tested using the improved R-centernet algorithm. The experimental results show that the improved algorithm improves the accuracy by 3.8%, the recall by 1.2% and the detection speed by 12 frames/second compared with the original R-centernet algorithm.

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“…Recently, there has been a significant increase in the utilization of deep learning models [53]. 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.…”

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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Evaluating the Impact of External Support on Green Building Construction Cost: A Hybrid Mathematical and Machine Learning Prediction Approach

Cited by 36 publications

References 70 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

Application of Minnan Folk Light and Shadow Animation in Built Environment in Object Detection Algorithm

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

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