Intelligent Construction Research Articles

Application of AI in Construction

At present, the application of BIM is in the ascendant, and 5G, the Internet of Things, and big data are also rising. AI is gradually influencing and innovating the construction field, and releasing the productivity of the industry from BIM, Internet of Things, big data and other aspects. The paper starts from the current situation of the construction industry, focusing on the integration of building construction, and tells how AI helps design in the early stage of architectural design to intelligent construction assistance in the later stage of the building. The use of AI can bring about the liberation of labor, improve the efficiency of the building from the beginning of the design to the final construction of the process and other advantages, and there are shortcomings such as excessive costs. In the future, AI applications may develop further when the time benefits can outweigh the cost benefits.

Earthwork digital twin for teleoperation of an automated bulldozer in edge dumping

AbstractThe unstructured construction site with a complex and changeable environment brings challenges to human–machine interaction. In this study, the earthwork digital twin (DT) is proposed to realize accurate and real‐time perception, which can support the teleoperation of an automated bulldozer. The overall framework includes anterior‐time DT, real‐time DT, and posterior‐time DT, which integrates three‐dimensional modeling, edge detection, and blade trajectory algorithms. Multidimensional heterogeneous data in bulldozer teleoperation can be displayed in real‐time to ensure construction safety. Big data during teleoperation of the construction process can be collected, stored, and analyzed. The DT proposed in this study was successfully applied in a major construction project in China, and testing results show its universality, robustness, and advanced performance. The key technologies proposed in this study can be applied to solve the common problems in the construction industry, which is promising for future intelligent construction.

Analysis of the safety impact of mobile rail hydraulic spreader on residential building machines under the system of building machines

The construction industry in China is a labor-intensive industry, but the cheap labor force has begun to change in recent years, with labor costs rising. In some labor exporting provinces, there has even been a "battle for talent.". Based on this, the "14th Five Year Plan" construction industry development plan issued by the Ministry of Housing and Urban-Rural Development proposes to accelerate the coordinated development of intelligent construction and industrialization of new buildings, and promote digital design, intelligent production, and intelligent construction. With the continuous progress of science and technology, the construction industry has also made many technological innovations, and innovative changes have taken place in related technologies. The residential building machine has begun to replace the traditional climbing frame application. Through gradual improvement, the lightweight building machine applicable to this project is only 220T, and the combination of spreader and building machine makes the concrete pouring process more convenient and intelligent, significantly reducing the labor intensity of construction personnel. Based on this, the analysis of the impact of mobile rail hydraulic spreader on the safety of residential building machines is a key factor.

Research And Analysis of Intelligent Construction Platform Technology for High-Rise Buildings

In recent years, with the rapid development of China's society and the continuous improvement of scientific and technological power, construction technology has been constantly innovated, from the traditional scaffolding to slipform, and then to climbing mold, lifting mold, construction mechanization, standardization and efficiency has been increasing. The high-rise intelligent integrated construction platform came into being as a "mobile factory" in the air, reducing the labor intensity of workers and improving the overall safety and operational efficiency of the construction. This paper starts from the basic principle of intelligent integrated construction platform, combined with Lanzhou Poly-Tianhui project, which is studied and analyzed from the aspects of design, construction and safety assurance, and systematically expounds the technology related to intelligent integrated construction platform of high rise.

Modularization Study of Intelligent Construction of Medical and Nursing Home Based on Assembly Building

Modularization Study of Intelligent Construction of Medical and Nursing Home Based on Assembly Building

Interlayer adhesion of 3D printed concrete: Influence of layer stacked vertically

3D printed concrete is an emerging intelligent construction method, and it has been attracting an exponential attention in this sector. However, one of the major issues in printed concrete is the cold joint between the neighbor layers. In this study, an experimental study on the relation between the number of stacked layers vertically and the interlayer adhesion of 3D printed element was conducted. Splitting method was carried out to test the interlayer bond strength, mercury intrusion porosimetry (MIP) and X-ray computed tomography (X-CT) scanner were used to investigate the pore structure of the interlayer zone. The experimental results indicated that a markable influence of the stacked layers vertically on the interlayer adhesion is found. The interlayer bond strength in the middle interface of the printed element is higher than that of the upper and bottom interfaces, especially the bond strength in the bottom interface is the least. Such result is also reflected in the results of the MIP test: the lower total pore volume and total pore surface area existed in the middle interfaces. The X-CT results showed that a large number of elongated and flat pores present in the interlayer zone, the pores in the bottom interface tend to be interconnected or continuous due to the pressure of upper printed layers. An empirical model was proposed to account for the formation of the irregular pore and the change of bond strengths between the vertical neighbored layers of 3D printed element.

Retracted: Application of BIM Technology in Data Collection of Large-Scale Engineering Intelligent Construction

Retracted: Application of BIM Technology in Data Collection of Large-Scale Engineering Intelligent Construction

The digitization work of cement plant in China

This article is to introduce the digitization work of smart cement plant in China. To save power and promote the product quality, delicacy management in variety ways of digital transformation is the best tool for the new built, or existing cement plants. New technologies, such as special integrated robots, online equipment, information plant form, cement process digital applicant (SAAS), Artificial Intelligence (AI) and Advanced Process Control (APC) are implemented to approve the traditional ways of operation.This paper introduces the application of intelligent factory technology in China, and focuses on the intelligent optimization control system. The system has self-learning and global optimization functions, with strong adaptability and robustness. In combination with quality prediction, kiln condition identification and quantitative identification of fire watching TV video images, the kiln system is feed-forward controlled to solve the control problem of long time delay, large inertia and strong coupling of the burning system. The system gives the global optimal recommended value of the control target through calculation, with which to ensure the best control and operation of the system.With the application of comprehensive intelligent construction, the cement plant will usually save 2–5 % of energy and 20 % staff simplification, and make the management mode more concise and efficient. Meanwhile, the cement groups are also developing the production information plat form, by merging the Enterprise Resource Planning (ERP) data, sales business to lower down the total cost. This paper tries to introduce the digital efforts that are carried out in the smart cement plants and shows the methodology of implementation.

Intelligent Construction Monitoring Method for Large and Complex Steel Structures Based on Laser Point Cloud

Large and complex steel structures play a vital role in building construction. However, deviations between the design model and the actual construction state are inevitable, which seriously affects the quality and safety of building construction. In our study, an intelligent construction monitoring method for large and complex steel structures based on laser point cloud is proposed. Firstly, three-dimensional laser scanning technology is introduced to capture accurate and complete spatial information on steel structures. Then, considering the inconsistency of the coordinate system between the design model and the laser point cloud, the building information model (BIM) is converted into the point cloud model, and the datum unification of the two types of the point cloud is achieved by adopting a coarse-to-fine registration strategy. Finally, the spatial information of steel structures is extracted from the laser point cloud based on the as-designed model, and the distance deviation between the two models is analyzed to reflect the actual construction state. To demonstrate the applicability of the proposed method, the steel structures’ point cloud of the stadium and the high-speed railway station is captured by the terrestrial three-dimensional laser scanner. The experimental results demonstrate that the method can extract the deviation between the design model and the actual construction, to provide accurate data sources for the intelligent fine construction of steel structures.

Evaluation of Water Network Construction Effect Based on Game-Weighting Matter-Element Cloud Model

Water network construction is one of the important ways to solve complex water problems at present. It is crucial for the optimal allocation of water resources, flood control, disaster reduction, protection of water ecology, water security, and sustainable urban development. Accordingly, this study formulates an index system for assessing the efficacy of water network construction based on the Driving Force–Pressure–State–Influence–Response (DPSIR) model, taking into account the four dimensions of optimal allocation of water resources, flood control and disaster reduction in river basins, protection of water ecosystems, and intelligent water network management. The proposed index system comprises four key aspects, which are utilized to evaluate the effectiveness of water network construction efforts. Then, the game-weighting method and the matter-element extension method improved by the cloud theory established an evaluation model to evaluate and compare the water network construction effects of the two cities in Henan Province. Finally, the GM (1,1) model was used to evaluate the water network construction effects, and future trends were predicted. The results show the following: (1) On the whole, the effect of water network construction in the two cities is constantly improving; (2) There has been a significant improvement in the intelligent management of water networks. The main reason for this result is that the “Internet +” has promoted the intelligent construction of water networks; (3) The water ecological environment, flood control, and drainage capabilities continued to improve, which has largely guaranteed the basic security bottom line of urban development space; (4) The advancement and utilization of water resources has undergone gradual improvements over time, with key impact metrics centered on water supply safety factors and the development and utilization of water resources. As water supply sources continue to diversify, it is expected that the aforementioned situation will be ameliorated in the future; (5) The predicted value shows that the water network construction of the two cities can basically meet the planned value of each index when the water network construction reaches the planning level. This paper provides help to promote the sustainable use of water resources and ensure the sustainable development of cities.

Research and Application of Intelligent Construction in Prefabricated Building Construction

The rapid development of emerging technologies such as BIM has greatly promoted the intelligent development of prefabricated building construction. This paper adopts a research method that combines on-site investigation, field testing, and technological development to explore quality control in prefabricated building construction and the integration of prefabricated buildings with intelligent construction. Focusing on three key technologies in prefabricated building construction - component installation, on-site layout, and steel connection—the paper examines the technical principles, operational processes, and application effects. It also studies and develops intelligent construction-related technologies such as, accurate alignment of prefabricated components, intelligent layout technology and simultaneous self-locking connection technology for multiple steel. Additionally, two types of rapid steel layout equipment are developed. These findings provide references for the further integration of prefabricated concrete construction with intelligent construction.

Construction of law and economics litigation service platform based on multimedia retrieval

Abstract Legal economics uses empirical research to analyze and predict the effects of various legal system arrangements to better explain whether the actual effect of the law is consistent with the expected effect of the law. Artificial Intelligence (AI) has become the core driving force of the new round of scientific and technological revolutions, and the trend of cross-border integration is becoming more and more obvious. Therefore, the intelligent construction of a legal litigation platform is the only way for social development. Litigation service management is a platform that uses big data technology to process and facilitate the information development of legal litigation services. In the face of the rapid changes in legal litigation services, an intelligent litigation service management platform is needed to serve the public. Given the above defects, this paper proposes a law and economics litigation service management platform based on multimedia retrieval, including a litigation service management system, a queuing system, and an autonomous operating system. The litigation service management system is connected with the queuing system and autonomous operating system, respectively. We provide a litigation service management platform to provide efficient operations with humanized functions to assist and improve the service quality of legal litigation services. At the same time, we combined the current application status of AI in grassroots courts and the actual work to analyze the difficulties and problems faced, draw lessons from the successful experience of some courts in China, and find out the direction and path for the application and promotion of AI in grassroots courts.

Construction of intelligent multi-construction management platform for bridges based on BIM technology

ABSTRACT In order to improve the efficiency of bridge intelligent multi-construction management, this paper combines BIM technology to build an intelligent multi-construction management platform for bridges, and studies the realization of vibration signal processing, feature extraction, fault diagnosis and wireless communication functions. Moreover, this paper completes the extraction of vibration signal features from three aspects: time domain, frequency domain and time–frequency domain, and manages the bridge construction process according to the extracted features. In addition, this paper establishes the integration between the bridge template design optimization module and the BIM model through the API interface of the Revit platform, realizes the extraction of design information from the BIM model of the bridge and combines template attribute information and safety design specifications to process it. From the simulation evaluation results, it can be seen that the bridge intelligent multi-construction management platform based on BIM technology proposed in this paper can effectively improve the management efficiency of bridge intelligent construction.

A deep ensemble learning-driven method for the intelligent construction of structural hysteresis models

Accurate force–deformation hysteretic models for structures, components, and materials are essential for structural analysis. The development of an explicit mathematical model for hysteresis poses challenges in the fields of artificial model selection and precise and efficient calibration of key parameters. In recent years, deep learning-based implicit hysteresis model construction methods have been widely recognized. However, such methods exhibit instability and lack of generalization. By combining deep neural network feature extraction with physics-based explicit hysteresis models, it is possible to efficiently construct an accurate and stable force–deformation hysteresis model purely based on test data. Therefore, in this study, a sensitivity-guided long short-term memory neural networks (LSTM) method is proposed to extract the features of structural behaviors and predict the key parameters of explicit hysteresis models accurately and efficiently. Furthermore, an adaptive ensemble learning method based on multiple explicit hysteresis models is proposed to improve the generalization ability. Case studies on test results demonstrate that this method can accurately predict structural behaviors, which is more efficient than manual construction and has better generalization ability than classical data-driven hysteresis models.

Advancing Bridge Construction Monitoring: AI-Based Building Information Modeling for Intelligent Structural Damage Recognition

ABSTRACT Building Information Modeling (BIM) has emerged as a transformative technology in the construction industry, revolutionizing various aspects of the field. The integration of Artificial Intelligence (AI) techniques with BIM holds significant promise and is gaining momentum in interdisciplinary applications. In China, the construction industry has witnessed notable advancements through the convergence of BIM, AI, and cloud data. However, the current state of intelligent construction technology in China reveals certain limitations that hinder its comprehensive development. This study addresses these challenges by focusing on the design of intelligent recognition algorithms for monitoring structural damage during bridge construction. Previous research has primarily employed classical neural network algorithms, but these approaches have exhibited certain limitations. This paper proposes innovative improvement measures to overcome these limitations and demonstrates their effectiveness through practical arithmetic examples. Furthermore, to enhance the intelligence level of the BIM system, this study integrates the improved neural network recognition algorithm into the BIM framework. The integration enables the BIM system to recognize and assess bridge structure damage efficiently and accurately. The outcomes of this research provide valuable insights into advancing the field of intelligent construction technology, particularly in the context of bridge construction monitoring.

Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines

Rock mass conditions are extremely sensitive to tunnel boring machine (TBM) tunneling. Therefore, establishing a surrounding rock excavatability (SRE) classification system applicable to TBM tunnels. Accurately and intelligently identifying excavatability grades can also facilitate efficient TBM tunneling and intelligent construction. Specific excavation and penetration rates were used to evaluate SRE. Their correlations with geological and tunneling parameters were explored using the field data from two water conveyance tunnels in China with different lithologies. A high-precision empirical SRE classification system was constructed using TOPSIS for multi-objective decision-making, and it was verified using engineering cases. An intelligent identification model for SRE grades in the stable phase of a TBM excavation cycle was established using 12,382 TBM rock-breaking datasets and deep forest models. Ten characteristic parameters, e.g., total thrust, were selected as model input features. Hyperparameter optimization was achieved using the grid search method. Deep forest was compared with decision tree, random forest, support vector classifier, and deep neural network. The contribution of the model’s features was measured using random forest. The main conclusions are as follows: the proposed SRE classification method is feasible and matches well with the actual excavation. In the intelligent identification of SRE classification, the accuracy and F1 scores when using deep forest were 96.33% and 0.9581, respectively. Deep forest exhibited better grade identification performance than the four models. Among the ten input features, penetration is the most important feature for the model’s input, while the top shield cylinder rod’s chamber pressure is the least important. The findings can provide some references for SRE classification and prediction and intelligent TBM control.

Impact of intelligence on the carbon emissions of energy consumption in the mining industry based on the expanded STIRPAT model

The intelligent construction of the mining industry has become an emerging trend in its future development plans. As a traditional high energy consumption industry, its energy conservation and emissions reduction have become particularly urgent concerns. Therefore, it is of great significance to study the impact of intelligence on the carbon emissions from energy consumption in the mining industry. This paper first uses dynamic indirect energy emissions factors to build an accounting model for carbon emissions from energy consumption in the mining industry and calculates the related carbon emissions from 2001 to 2020 by sector. Then, an expandable stochastic environmental impact assessment (STIRPAT) model is established to analyze the impact of intellectualization on the carbon emissions intensity of China’s mining industry. At the same time, many other important factors are included in the model, and the model is fitted using ridge regression. The results show that the order of influence, from large to small, is as follows: energy intensity, industrial output value, per capita output value, the proportion of output value of large and medium-sized enterprises, per capita stock of fixed assets, average enterprise output value, R&D intensity and proportion of indirect energy. Each 1% increase in the above factors will increase the corresponding carbon emissions intensity by 0.2005%, −0.1271%, −0.1207%, 0.7467%, −0.0595%, −0.09813%, 0.1143% and −0.1419%, respectively. Then, the prediction values of the independent variables are obtained by using the scenario analysis method and grey prediction model, and 12 scenarios are set up to predict the carbon emissions intensity of the mining industry energy consumption from 2021 to 2035 by using the expanded STIRPAT model. By analyzing the prediction results, we conclude that Scenario 8, Scenario 9, Scenario 10, Scenario 11 and Scenario 12 can achieve the emissions reduction goals of the mining industry in 2025 and 2030; the optimal path of emissions reduction is given for various scenarios. Finally, a development strategy for emissions reduction in China’s mining industry is proposed.

HydroBIM—Digital design, intelligent construction, and smart operation

HydroBIM—Digital design, intelligent construction, and smart operation

Lightweight design of temporary support device of rectangular shield for the robot system of coal mine roadway excavation

Owing to the existing problems of the temporary shield support device in a coal mine, such as considerable component weight, difficult disassembly, and large chamber installation, the design of the temporary rectangular shield support device was optimized. A module division method combining the functional analysis method and a similar feature clustering method was proposed, which completes the division of the functional module and establishes the structural module. A structural optimization method of “variable density topology optimization, parameter sensitivity analysis and Optimal Space-Filling Design (OSF)” was proposed. A variable-density topology optimization model of the device was established using the variable-density topology optimization method. The key variables of the device were determined using a parameter sensitivity analysis. The sample points of the critical variables were generated using the optimal space-filling design method, the approximate response surface models of mass, displacement, and stress were constructed, and the optimal solution of the device’s weight was obtained under the constraint conditions of pressure and displacement. A lightweight matching method of modularization based on transportability, disassembly, maintainability, and reliability was proposed, and the optimal matching scheme for the volume and mass of the device was determined. As a result, the weight of the temporary shield support device was reduced by 11.2%, and the maximum stress was reduced by 13.4%, under the premise of ensuring reliability. It realizes the convenience of transportation and high efficiency of assembly and disassembly in different design requirements, which is of great significance to accelerate the intelligent construction of coal mine.

Theoretical and Technological Challenges of Deep Underground Energy Storage in China

Deep underground energy storage is the use of deep underground spaces for large-scale energy storage, which is an important way to provide a stable supply of clean energy, enable a strategic petroleum reserve, and promote the peak shaving of natural gas. Rock salt formations are ideal geological media for large-scale energy storage, and China is rich in salt rock resources and has a major shortage of energy storage space. Compared with the salt domes in other countries, the salt rock formations in China are typical lacustrine bedded salt rocks characterized by thin beds, high impurity content, and many interlayers. The development of large-scale energy storage in such salt formations presents scientific and technical challenges, including: ① developing a multiscale progressive failure and characterization method for the rock mass around an energy storage cavern, considering the effects of multifield and multiphase coupling; ② understanding the leakage evolution of large-scale deep underground energy storage caverns; ③ understanding the long-term performance evolution of large-scale deep underground energy storage caverns; ④ developing intelligent construction technologies for the deep underground salt caverns used for energy storage; and ⑤ ensuring the long-term function of deep underground energy storage spaces. The solution to these key scientific and technological problems lies in establishing a theoretical and technical foundation for the development of large-scale deep underground energy storage in China.