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Integrated ground penetrating radar and deep learning approach for rebar diameter classification in concrete elements

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Frontiers of Structural and Civil engineering 2025年第4期19卷 524-540页

作者： Mostafa KHEDR Mahmoud METAWIE Mohamed MARZOUK Integrated Engineering Design Management Program Faculty of EngineeringCairo UniversityGiza 12613Egypt Structural Engineering Department Faculty of EngineeringCairo UniversityGiza 12613Egypt

Traditional evaluation of reinforced rebar in concrete elements involves destructive methods that may harm the *** paper introduces a framework that adopts non-destructive techniques to classify rebar in reinforced concrete *** framework integrates Ground Penetrating Radar(GPR)with deep learning to automate rebar detection and analysis in concrete *** framework consists of four stages:Data sets Creation,Data sets Processing,Steel Rebar Detection Model,and Transfer *** deep learning models are tested to choose the highest-performing *** YOLO v8 model outperforms Faster R-CNN and YOLO *** selected YOLO v8 model is trained on experimental and site data and then tested on real data from the building to validate the model’s accuracy and ability to classify rebar *** GPR with deep learning can potentially improve the accuracy and efficiency of rebar detection in structural assessments.

关键词： reinforced concrete inspection non-destructive testing GPR rebar diameter classification deep learning YOLO v8

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An Analytical Network Process (ANP) Model for Choosing Optimal Public–Private Partnership (PPP) Contract Types for Infrastructure Projects 5th

An Analytical Network Process (ANP) Model for Choosing Optim...

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5th International Conference on Civil engineering and Architecture, ICCEA 2022

作者： Zaw, Su Lae Yee Likhitruangsilp, Veerasak Construction Engineering and Management Program Department of Civil Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789819940486

With the rapid social and economic development, a great deal of expenditure is crucial for the nation’s infrastructure development. Nevertheless, the governments of many developing nations have been experienced with the limited budget and technical inability to deliver effective public infrastructures. Public–private partnership (PPP) has been adopted as an alternative collaboration arrangement between the government and the private sector in many nations to overcome these challenges. PPP can be defined as a long-term contract between a public agency and a private entity for rendering public facilities, including design, construct, finance, operate, and manage the project. The PPP contract types can be classified by various factors. Different PPP options imply different levels of responsibility and risks to be assumed by the private operator. Deciding an appropriate PPP contact type is always a risk-taking task for the government. This decision-making depends upon several criteria, which directly contribute to project success. This paper proposes a multi-criteria decision-making model based on analytical network process (ANP). The proposed model can be employed to choose a PPP contract type that optimizes important criteria. The main input of the model is the priorities (weights) of the PPP contracts selection criteria, which are obtained from a series of questionnaire surveys. The model is applied to an actual PPP project for verification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Risk perception

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Development of a System for Measuring Surface Slope with Point Cloud Data 5th

Development of a System for Measuring Surface Slope with Poi...

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5th International Conference on Civil engineering and Architecture, ICCEA 2022

作者： Peansupap, Vachara May, Aye Myint Department of Civil Engineering Construction Engineering and Management Program Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789819940486

The article explored the development of a surface slope measurement system using 3D point cloud data. The floor slope condition is important for the residential building floor quality inspection. Insufficient slope can lead to surface deterioration and water pocket formation. Current slope measurement in floor quality inspection has problems: time-consuming because of the forming log for each elevation point to calculate, random area points are chosen to measure, and the result did not cover the whole floor area, the tools used in the conventional methods can produce the close result not the exact result for the floor area. The aim of the research is the developed a system that can be used as an assistant tool in floor slope inspection with 3D point cloud data so that the calculation can be applied to all areas of the floor. 3D point cloud data can be collected using a FARO 3D laser scanner and execution of the system from acquired data will be performed in MATLAB. The system produces the slope value of the inspected room as well as the direction of the slope to decide the correct direction of the drainage of the floor which is important to access the bathroom or balcony in the residential building. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floors

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Effects of Wall Angle between Small Strip Areas by Using Point Cloud Data 5th

Effects of Wall Angle between Small Strip Areas by Using Poi...

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5th International Conference on Civil engineering and Architecture, ICCEA 2022

作者： Peansupap, Vachara Theint, Khine Zin Zin Construction Engineering and Management Program Department of Civil Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789819940486

Floors, walls, and ceilings in building construction projects are essential to inspect for checking the quality. The internal wall inspection mainly includes evaluation criteria, measuring techniques, and evaluation items. Traditionally, measuring the right angles between two walls has only been done visually or with a traditional ruler approach, which takes a lot of time. This paper applies point cloud technology to analyze the evaluation of the angles with the use of the MATLAB programming platform. In contrast to the conventional approach, the tools that are typically used to identify the angles include an L-squared ruler that is 30 and 50 cm long. Therefore, this research focus is on the comparison of angle measurement from the full point cloud and partial point cloud (strip model). This research was mainly conducted in two approaches. Firstly, to evaluate the angle between the walls and floors by using the MSAC algorithm with full plane model input and then the second approach automatically cuts the input model again into smaller sizes and then measures its angle. Then the full model and the cut model are compared to check the effect of the angle results depending on the plane size. This research also attempted to examine the distance effect on the laser scanner between the wall and the scanner for extended analysis. From the analysis result, the proposed conceptual framework can be used to check the angle values by automatically cutting the full plane model and their effect on the different distances. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floors

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Point cloud classification and part segmentation of steel structure elements

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Neural Computing and Applications 2025年第6期37卷 4387-4407页

作者： Daif, Hassan Marzouk, Mohamed Integrated Engineering Design Management Program Faculty of Engineering Cairo University Giza12613 Egypt Structural Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt Badr City Cairo11829 Egypt

The classification and part segmentation of point clouds have gained significant attention in the field of artificial intelligence (AI), especially in the construction industry. However, addressing the dataset directly in AI models remains a challenge, as most existing methods are not well-suited for processing point cloud data. PointNet has emerged as an AI architecture algorithm. It transforms individual points independently to learn local and global features. This research aims to develop a comprehensive framework for classification and part segmentation for point clouds of steel structure elements. The framework enhances the accuracy of the PointNet algorithm, and it consists of three stages: (1) dataset creation;(2) model classification;and (3) part segmentation. First, the dataset creation procedure encompasses modeling steel columns, beams, and braces using Autodesk Revit software. For the classification dataset, a dataset comprising 580 columns and 920 beams is obtained. In the part segmentation dataset, five categories of steel braced frame elements are generated, yielding a total of 21,870 elements for braced frame structures. Several point cloud experiments have been applied, including adjusting the number of points in the point cloud, altering the batch size, and fine-tuning the number of epochs. These experimental settings were systematically investigated to identify the optimal combination that yields the highest (AI) model accuracy. PointNet model achieved 100% accuracy across all classification experiments, while part segmentation experiments reached up to 97.10% accuracy, with a mean intersection over union (MIOU) of 93.70%. The comprehensive analysis of the point cloud dataset is applied on an actual case study to demonstrate the practical features of the proposed research. © The Author(s) 2024.

关键词： Steel structures

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A New Approach Toward Hazard Mitigation by Designing Moveable Architectural Space 13th

A New Approach Toward Hazard Mitigation by Designing Moveabl...

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13th International Conference on engineering, Project, and Production management, EPPM 2023

作者： Kamali, Seyedehfatemeh Wang, Han-Hsiang Contruction Engineering and Management Program Department of Civil Engineering National Central University Taoyuan Taiwan

ISBN: (纸本)9783031565434

The aim of this research is to evaluate smart moveable structures’ potential in hazard mitigation for perilous land settlers. Spaces of moveable structures will accordingly be able to move, interact, and adapt themselves to the particular surrounding environment and users’ needs with the aid of the AI, acting as the brain of the architectural spaces making them possible to sense, process, make decisions, and even be taught. As a concise definition, the present research is a part of an ongoing study that discusses the possibility of defining a new way to overcome natural disasters by the means of computation in construction or AI to create the capability of environmental interaction, reconfiguration, and adaptation, as well as the determination of the kinetic architecture potential applications in hazard mitigation practices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Disasters

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Recommendation of Resource Allocation Decision Based on Bipartite Graph Network Structure

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 128-139页

作者： Pei, Wen Pan, Wen-An Huang, Jui-Chan College of Management Chung Hua University Hsinchu30012 Taiwan Ph.D Program of Management Chung Hua University Hsinchu30012 Taiwan Department of Industrial Engineering and Management National Kaohsiung University of Science and Technology Kaohsiung807618 Taiwan

In resource allocation decisions in business, fully understanding customers’ needs and preferences helps to maximise benefits. As a result, in the modern business environment, the design of customized recommendation systems has gained a lot of attention. To this end, the study designs a recommendation algorithm for resource allocation decision based on improved two-part graph network structure. In this algorithm, an improved K-means clustering algorithm is introduced to deeply mine potential information. The calculation of similarity between users is also optimised to assist the target user to find the real neighbouring users. The findings demonstrate that, in comparison to the other algorithms, the resource allocation recommendation algorithm based on improved bipartite graph suggested in the study has a greater hit rate. The hit rate of the suggested algorithm can reach 32.5% when the recommendation list length is 10, which is a 21.5% improvement over the collaborative filtering algorithm. The suggested algorithm’s popularity is only 39.1, which is 69.3 less than the collaborative filtering algorithm when the suggestion list length is 10. Furthermore, the suggested algorithm for resource allocation decision-making created by the research has a greater recommendation accuracy, more personalization, and diversity, as seen by the proposed algorithm’s mean Hamming distance of 0.976. Through an improved bipartite graph network, the algorithm can fully analyze the historical preference information of users, effectively capture the complex relationship between users and products, generate personalized recommendation lists, and improve user satisfaction and purchase conversion rates. It provides an effective recommendation role for resource allocation decisions in modern business and helps to create greater economic benefits. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Collaborative filtering

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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Information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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Digital Twin in Prefabricated Construction – Approaches, Challenges and Requirements 41

Digital Twin in Prefabricated Construction – Approaches, Ch...

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41st International Symposium on Automation and Robotics in Construction, ISARC 2024

作者： Rangasamy, Veerakumar Yang, Jyh-Bin Graduate Institute Program of Construction Engineering and Management Department of Civil Engineering National Central University Taiwan

ISBN: (纸本)9780645832211

Digital Twin (DT) is a virtual replica that mirrors physical objects, systems, or entities. In the construction sector, DTs play a crucial role in building management, optimizing energy usage, predicting maintenance, and enhancing building lifecycle management. Although effectively integrated into traditional construction practices, its application in the prefabricated construction (PC) field remains relatively limited. Despite its potential to transform the PC, a significant research gap exists in comprehensive literature addressing the approaches, challenges, and requirements specific to this field. This study aims to bridge this gap through a mixed-method research approach that includes bibliometric analysis and systematic review. Publications related to PC and DT are collected from the Scopus database, followed by bibliometric analysis to offer a quantitative insight into the current research trends, prolific authors, and geographical distribution. Furthermore, this study conducts a systematic review that qualitatively analyzes the objectives of DT applications in PC, explores its methodological approaches, identifies challenges and recommendations for practitioners and researchers, and suggests future needs. The findings highlight the pressing need for standardization, robust data management, and scalable solutions to navigate the complexities. Conclusively, this paper presents a comprehensive conceptual framework to facilitate the application of DT in various phases of PC. The critical limitation is the reliance on the publications on the Scopus database and its potential biases. Excluding the need to expand on more emergent publications, discussing empirical DT studies in PC to highlight interoperability and standardization needs is also required for future research. © 2024 ISARC. All Rights Reserved.

关键词： Cyber Physical System

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Assessment of the GHG performance of a gas plant through an interactive business intelligence dashboard

Assessment of the GHG performance of a gas plant through an ...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Al-Mohannadi, A.A. Masoud, M.A. Al-Naemi, B.N. Kucukvar, M. Engineering Management Program Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University Doha Qatar

ISBN: (纸本)9781032803722

This study investigated the greenhouse gas performance of an operating plant from a business intelligence perspective. 2017-2021 GHG data was collected from the plant, transformed, and loaded into Microsoft Power BI, which was used to build the data model. A dynamic visual dashboard was created for data analysis. The analysis was categorized into areas based on the requirements of the functional dashboard. These categories were GHG emissions per time period, top emitting process units, distribution of GHG emissions by pre-defined categories, and energy intensity. The dashboard showed stable emissions and energy intensity from 2018-2021. Dashboard analysis also demonstrated major emissions contributions from gas turbine generators and fired heaters, mainly from fuel I, highlighting key focus areas for future GHG reduction initiatives. GHG abatement strategies were proposed to provide direction for improvement. Although the dashboard was intended to aid strategic decision making, it can be adapted for greater insights and for operational and tactical decision making by using more data in the model and closer cooperation with data and process owners and operators. © 2025 The Author(s).

关键词： Greenhouse gas emissions

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