Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction

Alshboul, Odey; Shehadeh, Ali; Almasabha, Ghassan; Almuflih, Ali Saeed

doi:10.3390/su14116651

Cited by 61 publications

(27 citation statements)

References 60 publications

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“…As a result, regardless of the pipe diameter, the optimal pipe thickness rose (nearly doubles) as the soil depth increases from 2.4 m to 20 m. This example shows how designers and practitioners utilize Equation (1) to determine the best reinforced concrete pipeline for a given pipe geometry and soil depth. Thus, in construction projects, providing decision-support tools using machine and deep learning approaches is vital [37][38][39][40][41][42][43][44][45][46][47][48].…”

Section: Designers Aid In Selecting Optimum Rc Pipeline Thicknessmentioning

confidence: 99%

Optimization of the Structural Performance of Buried Reinforced Concrete Pipelines in Cohesionless Soils

et al. 2022

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Pipelines are widely used to transport water, wastewater, and energy products. However, the recently published American Society of Civil Engineers report revealed that the USA drinking water infrastructure is deficient, where 12,000 miles of pipelines have deteriorated. This would require substantial financial investment to rebuild. Furthermore, the current pipeline design practice lacks the guideline to obtain the optimum steel reinforcement and pipeline geometry. Therefore, the current study aimed to fill this gap and help the pipeline designers and practitioners select the most economical reinforced concrete pipelines with optimum steel reinforcement while satisfying the shear stresses demand and serviceability limitations. Experimental testing is considered uneconomical and impractical for measuring the performance of pipelines under a high soil fill depth. Therefore, a parametric study was carried out for reinforced concrete pipes with various diameters buried under soil fill depths using a reliable finite element analysis to execute this investigation. The deflection range of the investigated reinforced concrete pipelines was between 0.5 to 13 mm. This indicates that the finite element analysis carefully selected the pipeline thickness, required flexural steel reinforcement, and concrete crack width while the pipeline does not undergo excessive deformation. This study revealed that the recommended optimum reinforced concrete pipeline diameter-to-thickness ratio, which is highly sensitive to the soil fill depth, is 6.0, 4.6, 4.2, and 3.8 for soil fill depths of 9.1, 12.2, 15.2, and 18.3 m, respectively. Moreover, the parametric study results offered an equation to estimate the optimum pipeline diameter-to-thickness ratio via a design example. The current research outcomes are imperative for decision-makers to accurately evaluate the structural performance of buried reinforced concrete pipelines.

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Section: Designers Aid In Selecting Optimum Rc Pipeline Thicknessmentioning

confidence: 99%

Optimization of the Structural Performance of Buried Reinforced Concrete Pipelines in Cohesionless Soils

et al. 2022

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“…Researchers have utilized different methods of crack detection based on image processing techniques. Techniques such as extreme gradient boosting (XGBOOST), deep neural networks, and random forests have been used by various researchers in relevant studies [40]. Table 2 presents some of the relevant image processing models, data acquisition sources, and datasets, and their advantages and limitations are subsequently explained.…”

Section: Image Processing Models For Detecting Cracksmentioning

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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“…However, the model did not thoroughly examine the impact of input variables. An extreme gradient boosting model was used by Alshboul et al (2022) to forecast project expenditures.…”

Section: Introductionmentioning

confidence: 99%

Cost-Duration Variance Prediction (CDVP) System Using Explainable Bayesian Optimized Ensemble Machine Learning

elmousalami

2023

Preprint

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This research introduces an integrated system for a novel management concept called automated cost-duration variance prediction (CDVP) system which allows to the project managers to look ahead at project budget, duration, and its variances at the project completion. To obtain the optimal results of each algorithm, the proposed system conducted Bayesian optimization for each developed algorithm. As a result, Extremely Randomized Trees (ET) was the optimal model for duration prediction at 2.53% and 0.961 for Mean Absolute Percentage Error (MAPE) and adjusted determination coefficient (R*2), respectively. AdaBoost Regressor (ABR) was the best model for duration variance prediction with 8.970 MAPE, and 0.949 R*2. On the other hand, the optimal model for project cost was a Light Gradient Boosting Machine (LGBM) with 2.98% and 0.931 for MAPE and R*2, respectively. Extremely Randomized Tree (ET) was the best model for cost variance prediction with 4.151 MAPE, and 0.962 R*2. SHapley Additive exPlanations (SHAP) technique was employed to provide explanations for the key drivers that used as inputs for both cost and duration predictions.

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Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction

Cited by 61 publications

References 60 publications

Optimization of the Structural Performance of Buried Reinforced Concrete Pipelines in Cohesionless Soils

Optimization of the Structural Performance of Buried Reinforced Concrete Pipelines in Cohesionless Soils

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

Cost-Duration Variance Prediction (CDVP) System Using Explainable Bayesian Optimized Ensemble Machine Learning

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