Lunar In Situ Large-Scale Construction:Quantitative Evaluation of Regolith Solidification Techniques

Lunar habitat construction is crucial for successful lunar exploration missions.Due to the limitations of transportation conditions,extensive global research has been conducted on lunar in situ material processing techniques in recent years.The aim of this paper is to provide a comprehensive review,precise classification,and quantitative evaluation of these approaches,focusing specifically on four main approaches:reaction solidification(RS),sintering/melting(SM),bonding solidification(BS),and confinement formation(CF).Eight key indicators have been identified for the construction of low-cost and highperformance systems to assess the feasibility of these methods:in situ material ratio,curing temperature,curing time,implementation conditions,compressive strength,tensile strength,curing dimensions,and environmental adaptability.The scoring thresholds are determined by comparing the construction requirements with the actual capabilities.Among the evaluated methods,regolith bagging has emerged as a promising option due to its high in situ material ratio,low time requirement,lack of hightemperature requirements,and minimal shortcomings,with only the compressive strength falling below the neutral score.The compressive strength still maintains a value of 2–3 MPa.The proposed construction scheme utilizing regolith bags offers numerous advantages,including rapid and large-scale construction,ensured tensile strength,and reduced reliance on equipment and energy.In this study,guidelines for evaluating regolith solidification techniques are provided,and directions for improvement are offered.The proposed lunar habitat design based on regolith bags is a practical reference for future research.

Localization of Mobile Robot Aided for Large-Scale Construction Based on Optimized Artificial Landmark Map in Ongoing Scene

The effectiveness of mobile robot aided for architectural construction depends strongly on its accurate localization ability.Localization of mobile robot is increasingly important for the printing of buildings in the construction scene.Although many available studies on the localization have been conducted,only a few studies have addressed the more challenging problem of localization for mobile robot in large-scale ongoing and featureless scenes.To realize the accurate localization of mobile robot in designated stations,we build an artificial landmark map and propose a novel nonlinear optimization algorithm based on graphs to reduce the uncertainty of the whole map.Then,the performances of localization for mobile robot based on the original and optimized map are compared and evaluated.Finally,experimental results show that the average absolute localization errors that adopted the proposed algorithm is reduced by about 21%compared to that of the original map.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

Investigation of the Severity of Modular Construction Adoption Barriers with Large-Scale Group Decision Making in an Organization from Internal and External Stakeholder Perspectives

Modular construction as an innovativemethod aids the construction industry in transforming to off-site construction production with high efficiency and environmental friendliness.Despite the obvious advantages,the uptake of modular construction is not booming as expected.However,previous studies have investigated and summarized the barriers to the adoption ofmodular construction.In this research,a Large-ScaleGroupDecisionMaking(LSGDM)-based analysis is first made of the severity of barriers to modular construction adoption from the perspective of construction stakeholders.In addition,the Technology-Organization-Environment(TOE)framework is utilized to identify the barriers based on three contexts(technology,organization,and environment).The Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)and LSGDM models are both implemented for the first time to analyze the severity of the barriers to modular adoption based on questionnaire results from internal and external stakeholders in an organization.Finally,in this research,in-depth insights into the severity of barriers are gained,providing a reference for construction organizations to manage modular adoption.

Construction of Risk Assessment Application Model of Epidemic Disease in Large-scale Pig Farms

The application model of epidemic disease assessment technology for Web-based large-scale pig farm was expounded from the identification of epidemic disease risk factors, construction of risk assessment model and development of risk assessment system. The assessed pig farm uploaded the epidemic disease risk data information through on-line answering evaluating questionnaire to get the immediate evaluation report. The model could enhance the risk communication between pig farm veterinarian, manager and veterinary experts to help farm system understand and find disease risk factors, assess and report the potential high risk items of the pig farm in the three systems of engineering epidemic disease prevention technology, biological safety and immune monitoring, and promote the improvement and perfection of epidemic disease prevention and control measures.

Dynamic Coordinated Management Model of Programme in Large-Scale Construction Company

Large-scale construction companies develop rapidly when facing more and more opportunities and challenges of programme contracting. However, project management technology, not meeting the requirements of firm growth, still stands proud, while relevant programme management methods have not been well studied. Therefore, this paper commences innovative application of coordinated management theory to programme management according to the need of changing the existing management mode in large-scale construction companies,then puts forward the dynamic coordinated management model composed of the dynamic coordination of function module, interface module, support module and evaluation module. Besides, coordination degree was introduced to make auxiliary analysis, so that companies can utilize internal and external resources more effectively to realize the goals of programme management as well as enterprise strategy.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Strategic Guidance Effect of Large-Scale Construction Enterprise Growth to Programme Contracting

In the case of single project management standing the major place, the management on the organizational level and project level is separated from each other in large-scale construction enterprises, and it is not long before program management theories applied to construction industry. Under the new situation of construction project clustering, how to choose projects has been the strategic decision for construction enterprises. Therefore,with the theory of program management and strategic management, this paper analyzes the relationship between the strategic growth of large-scale construction enterprises and program contracting, and attempts to provide meaningful ideas for enterprises to choose program from the strategic level.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Properties and Characteristics of Regolith-Based Materials for Extraterrestrial Construction

The construction of extraterrestrial bases has become a new goal in the active exploration of deep space.Among the construction techniques,in situ resource-based construction is one of the most promising because of its good sustainability and acceptable economic cost,triggering the development of various types of extraterrestrial construction materials.A comprehensive survey and comparison of materials from the perspective of performance was conducted to provide suggestions for material selection and optimization.Thirteen types of typical construction materials are discussed in terms of their reliability and applicability in extreme extraterrestrial environment.Mechanical,thermal and optical,and radiation-shielding properties are considered.The influencing factors and optimization methods for these properties are analyzed.From the perspective of material properties,the existing challenges lie in the comprehensive,long-term,and real characterization of regolith-based construction materials.Correspondingly,the suggested future directions include the application of high-throughput characterization methods,accelerated durability tests,and conducting extraterrestrial experiments.

A comprehensive review of lunar lava tube base construction and field research on a potential Earth test site

The Moon,as the closest celestial body to the Earth,plays a pivotal role in the progression of deep space exploration,and the establishment of research outposts on its surface represents a crucial step in this mission.Lunar lava tubes are special underground caves formed by volcanic eruptions and are considered as ideal natural shelters and scientific laboratories for lunar base construction.This paper begins with an in-depth overview of the geological origins,exploration history,and distribution locations of lunar lava tubes.Subsequently,it delves into the presentation of four distinctive advantages and typical concepts for constructing bases within lava tubes,summarizing the ground-based attempts made thus far in lunar lava tube base construction.Field studies conducted on a lava tube in Hainan revealed rock compositions similar to those found during the Apollo missions and clear lava tube structures,making it a promising analog site.Lastly,the challenges and opportunities encountered in the field of geotechnical engineering regarding the establishment of lunar lava tube bases are discussed,encompassing cave exploration technologies,in-situ testing methods,geomechanical properties under lunar extreme environments,base design and structural stability assessment,excavation and reinforcement techniques,and simulated Earth-based lava tube base.

Subsurface multi-physical characterization of mountain excavation and city construction in loess plateau with a fiber-optic sensing system

Mountain excavation and city construction(MECC)projects being launched in the Loess Plateau in China involve the creation of large-scale artificial land.Understanding the subsurface evolution characteristics of the artificial land is essential,yet challenging.Here,we use an improved fiber-optic monitoring system for its subsurface multi-physical characterization.The system enables us to gather spatiotemporal distribution of various parameters,including strata deformation,temperature,and moisture.Yan’an New District was selected as a case study to conduct refined in-situ monitoring through a 77 m-deep borehole and a 30 m-long trench.Findings reveal that the ground settlement involves both the deformation of the filling loess and the underlying intact loess.Notably,the filling loess exhibits a stronger creep capability compared to underlying intact loess.The deformation along the profile is unevenly distributed,with a positive correlation with soil moisture.Water accumulation has been observed at the interface between the filling loess and the underlying intact loess,leading to a significant deformation.Moreover,the temperature and moisture in the filling loess have reached a new equilibrium state,with their depths influenced by atmospheric conditions measuring at 31 m and 26 m,respectively.The refined investigation allows us to identify critical layers that matter the sustainable development of newly created urban areas,and provide improved insights into the evolution mechanisms of land creation.

Research Progress and Prospects of Total Factor Productivity in the Construction Industry

The high-quality development of the construction industry fundamentally stems from the significant improvement of total factor productivity.Therefore,it is of crucial significance for promoting the development of the construction industry to a higher level by scientifically and accurately measuring the total factor productivity of the construction industry and deeply analyzing the influencing factors behind it.Based on a comprehensive consideration of research methods and influencing factors,this paper systematically reviews the existing relevant literature on total factor productivity in the construction industry,aiming to reveal the current research development trend in this field and point out potential problems.This effort aims to provide a solid theoretical foundation and valuable reference for further in-depth research,and jointly promote the continuous progress and development of total factor productivity research in the construction industry.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Intelligent Sensing and Control of Road Construction Robot Scenes Based on Road Construction

Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.