The progress of our society is reflected in the building sector, which emphasises the necessity of constantly modifying our instruments to take advantage of new opportunities. An example of cutting-edge technology with the potential to completely transform the construction sector is the Internet of Things (IoT). The goal of this comprehensive analysis is to help the construction industry improve the understanding of how crucial it is to embrace IoT. In this study, a systematic review of the relevant literature was conducted to identify the factors that contribute to enhancing IoT applications in the construction industry. The primary objective was to list and evaluate the most important uses, advantages and difficulties of using the IoT in the building sector. This systematic review revealed that the IoT has significant potential to transform the construction industry by improving productivity, safety, sustainability and quality across the entire construction lifecycle. However, barriers such as data privacy and cybersecurity and a lack of standardised protocols need to be addressed. The review concludes that the IoT is likely to revolutionise the construction sector in the coming years if these challenges can be overcome. These findings imply that construction firms need to experiment with IoT and analytic tools across phased use cases, whilst policy and industry groups must collaborate on technology standards and protocols. Although obstacles exist, strategic IoT implementation promises major operational breakthroughs in the construction sector in the near future. 

A survey-based approach of framework development for improving the application of internet of things in the construction industry of Malaysia

Due to the flexibility and ease of deployment of Field Programmable Gate Arrays (FPGA), more and more studies have been conducted on developing and optimizing target detection algorithms based on Convolutional Neural Networks (CNN) models using FPGAs. Still, these studies focus on improving the performance of the core algorithm and optimizing hardware structure, with few studies focusing on the unified architecture design and corresponding optimization techniques for the algorithm model, resulting in inefficient overall model performance. The essential reason is that these studies do not address arithmetic power, speed, and resource consistency. In order to solve this problem, we propose a deep learning acceleration core architecture based on FPGAs, which is designed for target detection algorithms with CNN models, using multi-channel parallelization of CNN network models to improve the arithmetic power, using scheduling tasks and intensive computation pipelining to meet the algorithm’s data bandwidth requirements and unifying the speed and area of the orchestrated computation matrix to save hardware resources. The proposed framework achieves 14 Frames Per Second (FPS) inference performance of the TinyYolo model at 5 Giga Operations Per Second (GOPS) with 30% higher running clock frequency, 2–4 times higher arithmetic power, and 28% higher Digital Signal Processing (DSP) resource utilization efficiency using less than 25% of FPGA resource usage.

/pdf/fpga-implementation-of-a-deep-learning-acceleration-core-2d5bk4mr.pdf

FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection

Underwater Surface Defect Recognition of Bridges Based on Fusion of Semantic Segmentation and Three-Dimensional Point Cloud

Civil infrastructure over the years has experienced a dominant reliance on concrete material compared to other construction materials. Human inspection is the main mode of inspection for such structures, including concrete columns, which has been proven to be inaccurate and time-consuming. Convolutional neural networks (CNNs) are a substitute for such problems for both detection and quantification. However, storing the results and visualizing them at a later stage has always been a challenge. Additionally, integration of the concrete crack deep learning model to a mobile platform is an area that has received less attention. This study focuses on segmenting the concrete crack sections using the latest state-of-the-art (YOLOv7) neural network, which is then used to obtain the quantification data about the length and width of the detected crack using image binarization, and finally the results are published using a reporting system integrated to a mobile platform using a web and IoT system. The published report uses a checklist from the quantification results to grade the crack as well as its structure. The results show a mAP of 0.85, while the quantification results show a 10.82% absolute error, respectively. The reporting system takes a combined average of 5940 ms to store the data inside a database, which is then published through a mobile device. It has been demonstrated through this study that an automatic mobile reporting system is feasible to be used on buildings for maintenance, which can be further applied across other sectors of construction for monitoring and repair purposes. 

/pdf/cnn-based-automatic-mobile-reporting-system-and-13ibvu48.pdf

CNN-Based Automatic Mobile Reporting System and Quantification for the Concrete Crack Size of the Precast Members of OSC Construction

Local scour is one of the main reasons for bridge collapse. To solve the difficult problem of detecting the local scour depth of underwater pier structures, this paper explores an optimal method for predicting the local scour depth of underwater pier structures based on various ensemble learning methods. Firstly, this paper collects 487 sets of data samples containing nine input parameters with corresponding scour depths from the open-source database in the practical project. Secondly, this paper employs five algorithms commonly used in ensemble learning, that is, Random Forest (RF), Gradient Boosted Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM), to build a prediction model of the local scour depth. In addition, the Bayesian hyperparameter optimization method is applied to search for the best hyperparameter combination of the model. Then, eight evaluation indices, including Mean Absolute Error (MAE), Mean Bias Error (MBE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), Percent Bias (Pbias), and Willmott Index (WI), were used to compare and analyse the established prediction model, and the importance coefficients of each input parameter were evaluated based on this prediction model. Finally, Conditional Generative Adversarial Network (CGAN) was applied to augment and supplement the samples in the existing database, and the prediction model was used to verify its effectiveness. The results of this paper show that the parameter-optimized LightGBM model achieves the best prediction performance. Moreover, the established CGAN model can effectively solve the problem of insufficient data samples and lack of specific sample data.

Prediction of local scour depth in bridge piers: Physical information and machine learning based modeling

The number of bridges in operation has increased. Along with the increase in the length of time bridges are in service, the structural safety of the bridges also decreases. Bridge substructure is a key component of bridges, but there are few studies on safety management and identification of water bridge substructure damage. Deep learning is a focus of research in the field of target detection, and this document lightens YOLO-v4 to achieve precise and intelligent determination of concrete cracks. This was combined with a point cloud algorithm to provide a three-dimensional estimate of faulty lesions. Finally, the BIM was combined with the method of identifying the underwater structure of the deck. Based on Revit, an integrated management system for underwater bridge structures is put in place. Performing detailed bridge damage management includes (1) 3D visualization of the bridge detail model view, (2) establishment of a bridge damage database, (3) bridge damage management, and (4) management of the comprehensive underwater bridge inspection cycle.

/pdf/identification-of-underwater-structural-bridge-damage-and-1lqer7qu.pdf

Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mengpu Wei

Author Tools

Chat about Author

Papers

Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management