Improve Construction Efficiency Research Articles

Automatic lift path planning of prefabricated building components using semantic BIM, improved A* and GA

PurposePrefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).Design/methodology/approachFirstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.FindingsThe proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.Originality/valueIn this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

Mottainai in civil engineering – A message from Japan

This article examines the impact of Japan’s mottainai concept on civil engineering practices, focusing on the waste of materials, financial resources, and harmonisation of structures with the environment. The authors highlight the global challenges of environmental and social imbalances in the context of global warming. The Japanese concept of mottainai is presented as the key to shaping the civil engineering approach. The inefficient use of raw materials was analysed in terms of material waste. A transition to a closed-loop economy is recommended, emphasising the need for resource efficiency. In the context of financial resources, the need for efficient budget allocation and project management is emphasised. The section on harmonising structures with the environment focuses on aesthetics, environment, sustainable mobility, and an integrated approach to urban planning. Civil engineering projects should combine functional efficiency with respect to the environment, and the mottainai concept can be a tool for achieving sustainability in civil engineering. Innovative solutions, such as intelligent energy management systems, are recommended to improve construction efficiency.

Bond behavior between bundled composite bars and concrete using beam-end pullout tests

Bundled reinforcement can improve construction efficiency by reducing the number of connectors required. The steel-fiber reinforced polymer (FRP) composite bar (SFCB) is a novel reinforcement with high strength, moderate modulus of elasticity and good durability. Four groups of pullout tests were carried out, with parameters including the number of bars within each bundle, bonded length, and rebar type (steel bar, BFRP bar, SFCB). The failure modes of bar pullout without or after yielding, rebar fracture, and concrete splitting were observed. Compared to the single-bar specimen, the two-bar and three-bar bundled specimens resulted in approximately 30% and 40% decrease in average bond strength. The yielding slip sy of the three-bar bundled SFCB specimen is 3.8 times that of the single-bar specimen, indicating that bundled reinforcement can effectively control sy. A simplified bond strength prediction method and a bond stress-slip constitutive model based on the equivalent effective area approach are proposed. Finite element (FE) models were established and verified, and the failure modes of bar fracture are obtained by varying the bonded lengths for different bundled reinforcements, the ultimate slip of four-bar bundled BFRP bars increased by 127%. Recommendations for the development length of bundled SFCBs are proposed based on the FE results, which provide enough safety redundancy.

A hybrid building information modeling and collaboration platform for automation system in smart construction

Building information modeling (BIM) technology can organically combine data modeling and virtual reality, and compare them with actual construction objects to realize the smart collaboration of entity and the model in the construction processes, greatly reducing early stage mistakes and improve construction efficiency. This paper proposed an automation system framework of smart construction based on BIM technology and collaboration platform, and discussed the element configuration of smart construction based on BIM technology and collaboration platform, and finally elaborated the working mechanism of automated system based on BIM technology and collaboration platform. The study results show that the automation system based on the BIM technology and collaboration platform supports the rapid construction of large-scale three-dimensional scenes and the precise integration of multi-source data, and can bring together construction information and asset information from different sources to form an open, safe, and accessible digital environment. The application of smart construction technology in construction cost management has brought huge economic benefits, construction efficiency has been increased by 65%, construction period has been shortened by 30%, labor intensity has been reduced by 27%, and labor productivity has been increased by 39%, which has brought considerable indirect benefits to construction projects.

Behavior of drying and plastic shrinkage of Portland cement concrete prepared and cured under harsh field

The shrinkage of concrete is crucial under aggressive environmental conditions to avoid short and long-term deterioration. ACI committee for hot weather concreting specifies 35 °C as the maximum limit of fresh concrete to mitigate problems associated with that. This paper aims to investigate the plastic and drying shrinkage characteristics and compressive strength development of ordinary Portland cement (OPC) by varying w/c (0.3–0.45), casting temperatures (25–45 °C), and curing methods. The results demonstrated that the characteristics of concrete are significantly influenced by w/c, as well as the conditions at which they are cast and cured. In general, the plastic/drying shrinkage strains were observed to be minimum, while compressive strength was maximum when mixtures were cast at an ambient temperature of 32 °C. An increase in the w/c results in a corresponding rise in plastic/drying shrinkage strain, coupled with a decrease in the compressive strength of the mixtures. In addition, application of curing compound was found most effective in the field for enhancing the shrinkage characteristics whilst wet burlap practice was found beneficial in long-term compressive strength gain. These findings will enhance the optimization of mix designs for harsh conditions, improve construction efficiency, and support long-term concrete structure performance and sustainability.

3D laser scanning for predicting the alignment of large-span segmental precast assembled concrete cable-stayed bridges

Reasonable alignment control can simplify the bridge construction process, reduce the complexity and cost of construction, and improve construction efficiency. The prefabricated assembling construction method brings challenges to alignment control. This study proposes a method for predicting the alignment of concrete cable-stayed bridge assemblies in unstressed conditions to accelerate the construction process. The method uses the target paper to identify the 3D coordinates of control points and a coarse-to-accurate alignment method to achieve the virtual preassembly of precast concrete sections. The effectiveness of the method is demonstrated by comparing the errors in key parameters between virtual and physical preassembly and the predicted alignment results obtained through 3D coordinate conversion with the designed precast alignment. The method was applied to the construction of a large-span concrete cable-stayed bridge for a high-speed railroad in Guangdong, China, and the construction period was shortened by about 42 days.

Application of digital intelligent construction in the field of water conservancy and hydropower engineering

The application of intelligent construction technology is gradually penetrating the field of water conservancy and hydropower engineering. This article systematically summarizes the application research of intelligent construction in water conservancy and hydropower engineering, including intelligent design, intelligent construction, and intelligent monitoring. In terms of intelligent design, the use of BIM technology to establish three-dimensional modeling of water conservancy and hydropower projects can achieve collaboration, communication, and management of multiple aspects of information and improve design efficiency and quality in terms of intelligent construction. The use of robot technology and automation equipment can achieve construction automation and digitization, reduce human errors and safety risks, and improve construction efficiency and quality. In terms of intelligent monitoring, real-time monitoring, and intelligent management of water conservancy and hydropower projects can be achieved through the Internet of Things and big data technology, timely detection of problems, and early warning processing, ensuring the safe and stable operation of the project. A sound management system is established with standard specifications to promote the widespread application of intelligent construction in the field of water conservancy and hydropower engineering.

Design of a Mobile Manipulator for Curtain Wall Installation

The installation of the high-altitude curtain wall is mainly completed manually with simple mechanical assistance. The construction process is characterized by high labor intensity, high risk, low efficiency and low quality. To improve the automation and intelligence of curtain wall installation, improve construction efficiency and reduce labor intensity, a mobile manipulator for curtain wall installation is developed. Firstly, the curtain wall installation process is analyzed. Secondly, kinematics modeling is established, and the forward and inverse kinematics are analyzed and solved. Finally, the experiment is carried out to complete the tasks of grasping, rolling over and transporting stone, glass, ALC plate and other dense and semi-dense materials.

A dynamic learning method based on the Gaussian process for tunnel boring machine intelligent driving

Introduction: The application of intelligent learning methods to the mining of characteristics and rules of time-series data has gained increasing attention with the rapid development of deep learning. One critical application of such methods is the intelligent assistant driving of tunnel boring machines (TBMs), for which the optimization of driving parameters is essential to improve construction efficiency. However, existing prediction models for TBM parameters are “static” and cannot dynamically capture parameter evolution during real-time driving cycles.Methods: In this study, we propose a novel dynamic learning model for TBM parameters by introducing the Gaussian process to address this problem. The model can learn decision-making experiences from historical driving cycles, dynamically update the model based on small sample data from current driving cycles, and simultaneously achieve driving parameter prediction. We focused on real-time prediction of TBM parameters in a tunnel project in western China.Results: The results show that the average relative errors of predicted total thrust and torque values were 1.9% and 2.7%, respectively, and the prediction accuracy was higher than that of conventional models such as random forest and long short-term memory. The model fully exploited updating of small samples of parameters, reducing the average time cost of the model to 29.7 s, which satisfies the requirements of efficient application.Discussion: The dynamic learning strategy of time-series data adopted in this study provides a reference for other similar engineering applications. The proposed model can improve the prediction accuracy of TBM parameters, thus facilitating the optimization of driving parameters and enhancing the construction efficiency of tunnels.Conclusion: In summary, this study establishes a dynamic learning model of TBM parameters that can dynamically capture parameter evolution and achieve accurate real-time driving parameter prediction. The proposed model can contribute to the development of intelligent assistant driving of TBMs and similar engineering applications.

Windows and Doors Extraction from Point Cloud Data Combining Semantic Features and Material Characteristics

Point cloud data have become the primary spatial data source for the 3D reconstruction of building engineering, where 3D reconstructed building information models can improve construction efficiency. In such applications, detecting windows and doors is essential. Previous research mainly used red-green-blue (RGB) information or semantic features for detection, where the combination of these two features was not considered. Therefore, this research proposed a practical approach to detecting windows and doors using point cloud data with the combination of semantic features and material characteristics. The point cloud data are first segmented using Gradient Filtering and Random Sample Consensus (RANSAC) to obtain the 3D indoor data without intrusions and protrusions. As input, the 3D indoor data are projected to horizontal planes as 2D point cloud data. The 2D point cloud data are then transformed to 2D images, representing the indoor area for feature extraction. On the 2D images, the 2D boundary of each potential opening is extracted using an improved Bounding Box algorithm, and the extraction result is transformed back to 3D data. Based on the 3D data, the reflectivity of building material is applied to differentiate windows and doors from potential openings, and the number of data points is used to check the opening condition of windows and doors. The abovementioned approach was tested using the point cloud data representing one campus building, including two big rooms and one corridor. The experimental results showed that accurate detection of windows and doors was successfully reached. The completeness of the detection is 100%, and the correctness of the detection is 90.32%. The total time for the feature extraction is 22.8 s for processing 2 million point cloud data, including time from reading data of 10.319 s and time from showing the results of 4.938 s.

Method for real-time prediction of cutter wear during shield tunnelling: A new wear rate index and MCNN-GRU

Cutter wear is one of the key factors influencing construction efficiency during shield tunnelling. Prediction of cutter wear can improve construction efficiency by reducing the times of cutter inspections in engineering practice. Evaluation of cutter life is vital for cutter wear prediction, however, existing cutter life indices can only estimate the health condition of all cutters on cutterhead on a holistic basis. A new index was proposed to evaluate cutter wear located at a specific installation position on cutterhead. A deep learning model integrating the index was developed for the estimation of accumulated cutter wear during real time shield tunnelling. The new index can be obtained by monitored field parameters and can predict cutter wear with historical wear patterns. The input and output data samples were reshaped for multi-step prediction. A shield tunnelling section in Guangzhou weathered granite was used for validation. The proposed method can help reduce the cost of cutter replacement by reducing the times of machine interventions. The method article is a companion paper to the original article [1].•Proposed index for prediction of cutter wear rate.•Deep learning model of 1D-CNN and GRU.•Multi-step cutter wear prediction.

Application of BIM in Tunnel Design with Compaction Pile Reinforced Foundation Carrying Carbon Assessment Based on Advanced Dynamo Visual Programming: A Case Study in China

Carbon emission assessment in civil engineering has gained worldwide attention due to the negative effects of greenhouse gases on the environment. Significant amounts of building materials and electric power are consumed during the construction of tunnels, causing the release of greenhouse gases into the atmosphere. In addition, building information modeling (BIM) can be utilized to realize the computerized design of tunnels, and improve construction efficiency. However, the traditional BIM software (Autodesk Revit) lacks tunnel components and is unable to directly create a three-dimensional tunnel axis. This paper adopted BIM to build a three-dimensional model of the tunnel components for tunnel and carried out batch parameterization and component lofting based on Dynamo visual programming. The BIM of the tunnel guided the construction procedures and improved the construction efficiency. Based on the emission coefficient method, we calculated the carbon emissions from each component and loaded them into the BIM during the parameterization process. After the tunnel modeling design was completed, a bill of quantities was obtained. Then, the carbon emissions from the whole tunnel construction were calculated according to the bill. Thus, the combination of BIM technology and tunnel engineering was realized; this has practical significance for reductions in emissions, and cleaner construction in relation to tunnel engineering.

Test and Bearing Capacity Calculation of a New Energy-Dissipated Precast Shear Wall

At present, most precast shear walls are implemented in accordance with the method equivalent to a cast-in-place structure, where the joint is complicated, and the construction is difficult. According to the characteristics of precast shear wall structures, the design method of “strong horizontal joints, weak vertical joints” for a precast shear wall structure is proposed. The existing horizontal joint method was used to form strong horizontal joints, and the damper was used to connect the vertical joints to form weak vertical joints. It can reduce the pouring of concrete and improve construction efficiency. Three types of specimens (Rectangle shape, T shape, L shape) were designed. Low cycle reciprocating load tests were carried out under different axial compression ratios (0.1, 0.3), and the bearing capacity and ductility of the specimens were studied. The results show that the precast shear wall structure has good mechanical properties. The ductility coefficient is close to or greater than three, and the maximum ductility coefficient is 3.62. The specimens have good ductility. The relative displacement of the damper is greater than 1.75 mm. All the dampers yield and dissipate energy, which improves the seismic performance of the structure and plays a good role in connecting the wall limbs. The strain distribution law of the specimen section is discussed, the simplified mechanical calculation model is given, and the calculation method of flexural capacity of a precast shear wall structure is put forward.

Studies of Urban Safety Improvement for Anti-Corrosion Smokestack Protection Based on LOP Strategies

With the continuous expansion of urban industry, there is a great risk of fire and explosion during the application of anti-corrosion protection in smokestacks construction. The safety improvement was proposed using Computational Fluid Dynamics (CFD) simulation including process design and comprehensive protective approach based on Layers of Protection (LOP) strategies. The safety of construction was improved by the development of flammable gas monitoring and an alarming ventilation control system widely used in urban regulatory network. The concentration of flammable gas in the hanging basket is controlled below 5.0% of the lower explosion limit with the possibility to reduce this further to as low as 1.0%. The threat of smokestacks explosion to urban safety is greatly reduced. This system has a certain reference value for the government and factories to use digital tools to improve construction efficiency and urban safety.

Stress-strain behavior of ECC-GFRP spiral confined concrete cylinders

This paper introduces a new confinement system that combines engineered cementitious composites (ECC) with glass fiber-reinforced polymer (GFRP) spiral as a protective cover to confine the concrete core. Meanwhile, the precast ECC-GFRP spiral cover also can serve as permanent formwork to improve construction efficiency and save construction costs. This study analyzed the confinement mechanism from the combination of ECC and GFRP spiral, and the effects of concrete strength and confinement stiffness on the stress-strain behavior of the confined concrete were investigated. The stress-strain behavior analyses confirm that the residual confining stress from ECC could enhance the ductility of the composite cylinder after GFRP rupture. Test results also indicate that an ECC-GFRP spiral layer has excellent crack control capability before peak loads, guaranteeing concrete cylinder durability. In addition, the model evaluation indicates that ultimate condition models for FRP-confined concrete are also applicable to ECC-GFRP spiral-confined concrete as long as the confining pressure from both ECC and GFRP spiral can be accurately predicted. A modified stress-strain model is proposed for this confinement form and performs well against test stress-strain curves.

Structural performance evaluation of pultruded GFRP composite space truss: Experimental study and numerical simulation

This paper presents space truss structures assembled from pultruded square hollow glass fiber reinforced plastic (GFRP) members. The novel steel connectors are used to connect the GFRP members, which can significantly improve construction efficiency. The structural performance of the four-span space truss and space truss unit is evaluated by the four-point bending test and the static test, respectively. The test results show that the GFRP space truss structure exhibited satisfactory overall stiffness and load-bearing capacity. The failure modes mainly include shear failure of the bolt, local buckling of GFRP profiles, and local cracks at holes and cuts. The structural stiffness of the space truss unit increases at the post-failure stage, which is caused by the compression buckling of the pultruded GFRP profile. The structural stiffness of the four-span space truss decreases at the post-failure stage, and the large nonlinear deformation capability provides an early warning before the ultimate collapse. In addition, a finite element model considering material nonlinearity is developed to predict the failure mode, overall structural stiffness, and load-carrying capacity of the GFRP space truss structure.

Weather window and efficiency assessment of offshore wind power construction in China adjacent seas using the calibrated SWAN model

Waves simulated by a high-resolution calibrated SWAN model of China adjacent seas over a long-term period (from 1996 to 2020) are utilized to investigate the characteristics of offshore wind power construction efficiency. The model is forced by validated ERA5 wind data, which shows better agreement with ICOADS observations than CCMPv2 and CFSR&CFSv2. During the calibration process, we focused on wind input, whitecapping, and bottom friction terms and on whitecapping dissipation and bottom friction coefficients. Statistical error analysis of the calibration results demonstrates that the underestimation of significant wave height simulated with default settings has been corrected and the model performance has been improved in the study area. The assessment results revealed that construction efficiency has significant spatial and temporal variation characteristics. Annual and monthly averaged workloads of pile foundation construction and wind turbine installation are distinctly different since their specific weather restrictions and durations. Generally speaking, weather conditions from May to August are more favourable for offshore construction. In winter, offshore construction is strongly suggested to be stopped especially for sea areas of Fujian and Guangdong. For selected projects, the key environmental factors affecting offshore operation are different, which should be considered to improve construction efficiency and further decrease cost.

Design and construction of Jinhai Bridge, China

Jinhai Bridge (China) is a cable-stayed bridge with a single deck carrying a double rail track in the middle and three highway lanes on each side. The structural design combines a rigid frame with continuous spans, effectively improving the overall structural rigidity to meet the requirements for railway traffic and minimising the temperature effect from a long continuous structure. The deck girder is a steel box with large cantilever flanges, which is cost effective. The steel pylons are designed with four legs located on top of the deck instead of on the pile cap in order to minimise the size of the pile foundation. To improve construction efficiency, the main girder and pylons were prefabricated into large-scale components and erected using heavy equipment.

Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach

A deep learning-based multi-output prediction model is developed to better understand and more accurately estimate tunnel boring machine (TBM) performance in each segment ring during the deep excavation under complex underground environments. The novelty lies in the development of a new deep learning approach named att-GCN, which feasibly integrates the graph convolutional networks (GCN) and scaled dot-product attention mechanism to improve model performance and interpretability. It is proved that our proposed att-GCN model is outstanding in significantly enhancing the prediction performance and effectively capturing the influence between monitoring points. As a case study, the proposed method is validated in a Singapore Mass Rail Transit (MRT) construction project, where seven features associated with the TBM machine are input for att-GCN training and testing. Experimental results reveal that the att-GCN model can exhibit a powerful capability in simultaneously predicting two targets named penetration rate (y1) and energy consumption (y2), reaching the mean absolute percentage error (MAPE) value at 15.475% and 15.173%, respectively. In terms of prediction accuracy, att-GCN is superior to some state-of-the-art algorithms, including deep neural network (DNN), random forest (RF), and support vector regression (SVR). Moreover, an online-learning version of att-GCN is designed. When the objective value is gradually known and fed into att-GCN during the tunneling procedure, the model can yield more impressive performance under the MAPE of 8.504% (y1) and 7.934% (y2). Accordingly, the real-time estimation of TBM performance based on the time-varying monitoring data provides valuable evidence to realize the intelligent control of TBM tunneling, which can ultimately improve construction efficiency and reliability.

Spherical harmonic shape descriptors of nodal force demands for quantifying spatial truss connection complexity

The connections of a spatial truss structure play a critical role in the safe and efficient transfer of axial forces between members. For discrete connections, they can also improve construction efficiency by acting as registration devices that lock members in precise orientations. As more geometrically complex spatial trusses are enabled by computational workflows and the demand for material-efficient spanning systems, there is a need to understand the effects of global form on the demands at the connections. For large-scale structures with irregular geometry, customizing individual nodes to meet exact member orientations and force demands may be infeasible; conversely, standardizing all connections results in oversized nodes and a compromise in registration potential. We propose a method for quantifying the complexity of spatial truss designs by the variation in nodal force demands. By representing nodal forces as a geometric object, we leverage the spherical harmonic shape descriptor, developed for applications in computational geometry, to characterize each node by a rotation and translation-invariant fixed-length vector. We define a complexity score for spatial truss design by the variance in the positions of the feature vectors in higher-dimensional space, providing an additional performance metric during early stage design exploration. We then develop a pathway towards reducing complexity by clustering nodes with respect to their feature vectors to reduce the number of unique connectors for design while minimizing the effects of mass standardization.