Multi-objective Analysis Research Articles

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Optimal planning and operation of heterogeneous autonomous and grid-connected microgrids based on multi-criteria techno-economic, environmental, and social indices

Sustainable energy transition involves the execution of recent technologies as a means of ensuring energy access and security. However, the increasing adoption of such technologies, particularly in the presence of diverse constraints, poses significant challenges from a planning perspective. In this context, the multi-objective analysis offers valuable insights for decision-making that balances mutual benefits, as relying solely on a single objective may increase the risk level for stakeholders engaged in coordinated decisions. Based on the real dataset, this study presents a comparative multi-criteria techno-economic, environmental, and social evaluation of site-specific unified standalone and grid-connected hybrid microgrids. The study employs a modified Last Cluster Mean Carried Forward approach for data processing, incorporating the Proprietary Derivative-free Algorithm and Original Grid-Search Algorithm to ensure a standardized comparison. Results reveal the substantial advantages of grid-connected systems over standalone counterparts, with reductions of 12.54 % to 63.73 % and 68.93 % to 89.13 % in terms of Net Present Cost and Levelized Cost of Energy, respectively. Grid-connected systems exhibit superior adaptability, recovering 52.3 % to 98.1 % surplus energy with a Renewable Fraction averaging 77.1 % to 87.9 %. However, these systems were hindered by frequent interruptions and required a minimum capacity shortage of 2.5 % to 3.5 %. Furthermore, grid-connected systems have proved feasible when carbon emissions, forests required to absorb the emissions, and the Social Cost of Carbon Emissions are considered in the range of 4.65–67.13 kiloton (metric), 423.32–6108.53 ha, and $0.24-$3.42 million, respectively. Social analysis and sensitivity analyses are performed to justify the robustness and adaptability. Lastly, the findings are followed by policy recommendations and results validation by comparing prevailing government tariffs and other studies.

Multi-Objective Numerical Analysis of Horizontal Rectilinear Earth–Air Heat Exchangers with Elliptical Cross Section Using Constructal Design and TOPSIS

This study presents a numerical evaluation of a Horizontal Rectilinear Earth–air Heat Exchanger (EAHE), considering the climatic and soil conditions of Viamão, Brazil, a subtropical region. The Constructal Design method, combined with the Exhaustive Search, was utilized to define the system constraints, degree of freedom, and performance indicators. The degree of freedom was characterized by the aspect ratio between the vertical and horizontal lengths of the elliptical cross-section duct (H/L). The performance indicators for the EAHE configurations were assessed based on thermal potential (TP) and pressure drop (PD). The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied for multi-objective evaluation, and a methodology for EAHE is proposed. The problem was solved using FLUENT software (version 2024 R2), which employs the Finite Volume Method to solve the conservation equations for mass, momentum, and energy. The (H/L)T,o = 6.0 configuration showed a 16.4% increase in thermal performance for heating and 15.9% for cooling compared to the conventional circular duct. Conversely, the (H/L)F,o = 1.0 configuration reduced pressure loss by 65.33%. The integration of Constructal Design with TOPSIS facilitated the identification of optimized geometries that achieve a balance between performance indicators and those that specifically prioritize thermal or fluid dynamic aspects, being this approach an original scientific contribution of the present work.

Multiple solutions of tungsten carbide-cobalt cutting tool turning performance optimization results by Taguchi's combined multiobjective approach analysis an exponentiated exponential Weibull Dagum distribution model

Manufacturers employ various techniques to enhance tool life and influence both production costs and surface finish quality. Recently, cryogenic treatment has gained attention for improving machining performance. In this study, tungsten carbide-cobalt (WC–Co) cutting tools underwent cryogenic treatment at −196 °C for 24 h followed by tempering at 200 °C for 2 h. Machinability characteristics including Tool Wear Rate and Surface Roughness (Ra) were assed for Cryo Treated (CT) cutting tool during spinning operations on Al6063 alloy. This evaluation was compared with an untreated tool. Results demonstrate that the CT tool significantly enhances tool longevity while ensuring a satisfactory surface finish. To optimize the performance of the CT tool in turning processes, multiobjective optimization techniques, namely Taguchi's Grey Relational Analysis and Technique for Order Preference by Similarity to Ideal Solution, were utilized. In this research exponentiated exponential Weibull Dagum (EEWD) model has been utilized with the goal of increasing the tractability of the DGM (Dagum) in modeling the real-world data. The main object of this work, an innovative generalization of the EEWD distribution of Quantile Function (QntFEEWD) has been derived with six parameters. This QntFEEWD is significantly influent in driving measures of position such as the Qutl (quartiles), Octl (octiles), Decl (deciles), Pecl (percentiles), and median of EEWD distribution. The T-R{.} framework has been recently used to generalize different distributions; however, the DGM distribution's viability has not been examined. The T-R{.} combines three distributions, with one acting as a standard distribution. Each distribution's combined potency, which is a weighted hazard function of the baseline distribution, would have more parameters but be able to handle dataset bimodality with great flexibility. In order to generalize the DGM distribution, this paper used the quantile function of the Weibull distribution. The current study presents the EEWD, a novel generalized six parameter (6P) model.

Application of the maturity model for collaborative scheduling for construction projects

PurposeThis paper introduces the weighting, analysis and validation method used in the development of the Maturity Model for Collaborative Scheduling (MMCS). The scoring and ranking process introduced by the MMCS fills a gap in the literature by supporting the selection of collaborative scheduling (CS) practices that yield more weight toward the achievement of higher maturity levels in the development and implementation of CS. The ranking process can then be used during pre/post project execution to track collaborative scheduling in practice against the model’s weighting and provide the project team with constructive feedback and actionable steps for reaching the next highest level of collaboration.Design/methodology/approachThe MMCS, which focuses on five pillars (key areas of interest for CS) and related swim lanes (specific attributes), covers a broad range of areas in the construction industry and was coded into a survey. The relative weights of pillars and swim lanes were then established using the Delphi method with the group of subject matter experts (SMEs), analyzed using multi-objective decision analysis (MODA) and validated using 241 answers to a survey with questions drawn from the MMCS, including organizations across the industry in the United States.FindingsThe project scoring defines bounds for bronze, silver and gold levels of collaboration in scheduling. Project evaluations can then be used to identify areas for continuous improvement and enhanced collaboration. We offer recommendations and best practices for project improvement.Originality/valueTwo original contributions resulted from this work: (1) a method to elicit weights based on a combination of Delphi, MODA and survey methods was used to develop and validate a scale with three different maturity levels to support the use and continuous improvement of CS practices and (2) a validated model was used to assess the maturity level of CS in construction projects alongside specific recommendations to move upward in terms of maturity. In practice, project leaders can use this model to assess project performance, advance the project’s maturity and guide continuous improvement efforts for enhanced collaboration.

Multi-objective grey correlation analysis based on CFRP Helical Milling simulation model

Multi-objective grey correlation analysis based on CFRP Helical Milling simulation model

Cold Coastal City Neighborhood Morphology Design Method Based on Multi-Objective Optimization Simulation Analysis

In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization method based on a multi-objective optimization algorithm, combined with tools such as Grasshopper, Ladybug, Honeybee and Wallacei, to provide scientific optimization decision intervals for morphology control and evaluation factors at the initial stage of coastal city block design. The effectiveness of this optimization strategy is verified through empirical research on typical coastal neighborhoods in Dalian. The results show that the strategy derived from the multi-objective optimization-based evaluation significantly improves the wind environment and thermal comfort of Dalian neighborhoods in winter and summer: the optimization reduced the average wind speed inside the block by 0.47 m/s and increased the UTCI by 0.48 °C in winter, and it increased the wind speed to 1.5 m/s and decreased the UTCI by 0.59 °C in summer. This study shows that the use of simulation assessment and multi-objective optimization technology to adjust the block form of coastal cities can effectively improve the seasonal wind and heat environment and provide a scientific basis for the design and renewal of coastal cities.

Selection of refrigerant based on multi-objective decision analysis for different waste heat recovery schemes

Waste heat generation, upgrading, and refrigeration are the fundamental ways to recover and utilize waste heat. The rationalizing the use of refrigerants also contribute in creating energy savings and minimizing carbon emissions. This study evaluated the thermodynamics, economics, and environmental of different types of refrigerants in three kinds of waste heat recovery schemes: generate electricity, heat pump, and refrigeration. Based on this, the entropy weight and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) were combined to assess the overall performance of the refrigerants. A thorough analysis revealed that R1234ze(E) can replace R245fa and R123 in the organic rankine cycle (ORC). The best refrigerant for vapor compression refrigeration (VCR) and high temperature heat pump (HTHP) systems is R1243zf. In addition, the multi-objective decision analysis shows that the performance difference among the nine selected refrigerants is the total cost, followed by the environmental impact. The approach is successful in recognizing the variations between different refrigerants in the same waste heat recovery scheme and gives a thorough evaluation. It sets instructions for the future use of eco-friendly refrigerants and their application waste heat recovery schemes.

Evaluation of entropy-coupled multi-criteria decision-making methods for enhancing machinability

Optimizing machining parameters is crucial, enhancing machinability while maintaining high product quality standards. This study bridges a critical research gap by evaluating and comparing five Taguchi-based Multi-Criteria Decision Making (MCDM) techniques—Combined Compromised Solution (CoCoSo), Grey Relational Analysis (GRA), Multi-Objective Optimization Ratio Analysis (MOORA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Complex Proportional Assessment (COPRAS)—coupled with the Entropy method to optimize machining parameters for enhancing machinability in turning medium carbon steel. The focus is on feed rate and cutting speed under dry and Minimum Quantity Lubrication (MQL) environments considering six critical machining responses: material removal rate, surface roughness, main cutting force, cutting temperature, cutting ratio, and tool life.The results reveal that MQL consistently improves machining performance, with COPRAS, TOPSIS, MOORA, and GRA converging on an optimal setting favoring an MQL environment, a 0.14 mm/rev feed rate, and a cutting speed of 137 m/min, whereas CoCoSo suggests a different optimal parameter setting. CoCoSo and GRA demonstrate the highest reliability, evidenced by minimal discrepancies between predicted and experimental results, with absolute percentage errors of 0.647 % and 0.659 %, respectively. The COPRAS method also shows strong predictive accuracy with a 5.573 % error, outperforming MOORA and TOPSIS. Spearman's rank correlation analysis reveals a high agreement between COPRAS, MOORA, and TOPSIS, with COPRAS emerging as a potential replacement for the latter in similar decision-making scenarios. SEM and EDX analyses confirm that MQL conditions reduce tool wear, enhance surface quality, and extend tool life compared to dry machining. This research provides insights into effective parameter optimization strategies for improving machinability and underscores the benefits of adopting MQL for sustainable manufacturing processes.

Multi-Objective Optimization Analysis of Electromagnetic Performance of Permanent Magnet Synchronous Motors Based on the PSO Algorithm

In order to optimize the electromagnetic performance of a permanent magnet synchronous motor (PMSM) during operation, this paper takes the size of the stator slot structure of the motor as the optimization variable and the peak cogging torque and no-load back electromotive force (EMF) amplitude of the motor as the optimization objectives. A multi-objective optimization method based on the particle swarm optimization (PSO) algorithm is adopted to obtain a structural parameter combination that minimizes the peak cogging torque and no-load back EMF amplitude while meeting the reasonable range requirements of magnetic flux density amplitude. The optimized motor structure design prototype is experimentally verified. The results show that through multi-objective optimization based on the PSO algorithm, the electromagnetic performance of the motor has been improved, with a reduction of 36.33% in peak cogging torque and 2.65% in peak no-load back EMF, indicating a reasonable magnetic flux density amplitude. The experimental results of the optimized prototype show that the difference between the theoretical simulation values and the experimental values is within a reasonable range, which verifies the effectiveness of the multi-objective optimization method.

Crashworthiness and optimization design of additive manufacturing double gradient lattice enhanced thin-walled tubes under dynamic impact loading

It is of paramount importance to ensure the protection of both infrastructure and personnel in scenarios characterized by a high strain rate dynamic impact. In order to develop lighter weight and higher crashworthiness energy-absorbing structures, this paper incorporates the bionic design of lattice reinforced thin-walled tube structures in the form of double gradient, as a means of achieving the aforementioned objectives. Material mechanical properties and the designed structures at high strain rate were researched using Split Hopkinson Pressure Bar (SHPB) experiment. The effects of gradient form and gradient parameters on the deformation pattern and crashworthiness of lattice enhanced thin-walled tubes are researched by means of the validated finite element model and the experimentally fitted Johnson-Cook material model. The results indicate that the specific energy absorption of the double gradient lattice enhanced thin-walled tube (T3L3) is increased by 36.19 % and the peak crush force is reduced by 21.19 % compared to the lattice enhanced thin-walled tube without gradient. Finally, the multi-objective optimization analysis of the T3L3 with the objective of achieving the maximum SEA and minimum PCF is carried out by Response Surface Methodology (RSM) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to find the Pareto optimal solution set. Double-gradient bionic structure offers a new idea for designing energy-absorbing structures under high-speed impact conditions.

French economy and clean energy transition: A macroeconomic multi-objective extended input-output analysis

The complexity of a sustainable economy is rooted in its socio-economic and environmental intricacies, particularly in formulating pathways for the harmonious integration of these parameters. This study introduces an extended input-output analysis and a multi-objective optimisation framework designed to discern trajectories for reducing CO2 emissions while simultaneously maximising GDP and employment. The economic alterations are evaluated through metrics facilitating the examination of both direct and indirect consequences stemming from perturbations within the economy. The focus of this research centres on the French economy, concentrating on pivotal sectors where reducing demand could yield the greatest reduction in CO2 emissions with minimal socio-economic ramifications. Additionally, a model is outlined for energy substitution, wherein fossil fuels in the French electricity mix are supplanted with clean energies. The ensuing effects of such a model on emission reduction pathways are scrutinised, followed by a comparison with the baseline case study.

Multi-objective optimization and analysis of divided-layer varying-network magnetic circuit based axial field flux-switching magnetic gear composite motors

Multi-objective optimization and analysis of divided-layer varying-network magnetic circuit based axial field flux-switching magnetic gear composite motors

Multi-objective analysis of the turbine blade internal cooling channel with discontinuous bionic S-shaped ribs

Through numerical simulation and experimental verification, the flow and heat transfer characteristics of different discontinuous bionic S-shaped ribs in cooling channels were studied. The influence mechanism of different discontinuous locations, discontinuous sizes, and discontinuous numbers coupling design on the channel performance is investigated in depth. The study demonstrates that the discontinuous design can significantly enhance flow field disturbance in the discontinuous region of the S-shaped rib, thereby substantially improving channel heat transfer. Channel heat transfer changes with the discontinuity location, exhibiting a trend similar to the cosine function. The channel heat transfer has different trends with the change of the discontinuity size under different discontinuity locations. Obtained by coupling the discontinuous size of 2P/10 with the discontinuous location of 6P/10 single discontinuous S-shaped rib heat transfer is the best, and the ribbed wall Nu/Nu0 is approximately 35 % higher than the continuous rib. The greatest thermal performance, which is 39 % higher than continuous ribs, is achieved by single discontinuous S-shaped ribs with discontinuous size and discontinuous location of 5P/10 and 9P/10, respectively. The decreased distance between discontinuities in double discontinuous S-shaped ribs leads to the flow field disturbance coupling effects of adjacent discontinuities, which further enhances the disturbance and improves heat transfer.

Study on the properties and components of solid-liquid products by co-pyrolysis of sludge and cotton stalk

The effects of sludge content and pyrolysis temperature on the pyrolysis process and product properties were studied through the co-pyrolysis of sludge and cotton stalk. Multi-objective fuzzy hierarchical analysis was used to analyze the multiphase properties of pyrolysis products, and describe the internal relationship between pyrolysis conditions and product properties. It has been proved using kinetic calculation that the mass ratio of sludge and cotton stalk is higher than 1:2 required the lower pyrolysis activation energy. According to the comparison between experimental and theoretical results, the addition of sludge is beneficial to increase the bio-oil yield. In terms of product performance, the addition of sludge and increase of pyrolysis temperature both can improve the specific surface area, pore volume of biochar and the pH value of bio-oil, reduce the total acid content, increase the total phenol content and optimize calorific value of bio-oil. The specific surface area of biochar, calorific value of bio-oil, total phenol content and pH were evaluated by multi-objective decision analysis method, the optimum process condition of sludge: cotton stalk = 2:1 at 600 ℃. Under the conditions, the specific surface area of biochar is 57.40 m2/g, the pH of the bio-oil is 8.06, the total acid content is 5.94 mg/g, the calorific value is 27.18 KJ/g and the total phenol content is 47.32 mg/g, respectively. Compared to the pyrolysis product of cotton stalks, the specific surface area is increased almost 20 times, the calorific value is increased by 13 %, the total acid content is decreased by 95 %, and the total phenol content is similar. This study proposed a preparation process of bio-oil with high performance, and provided a theoretical basis for the subsequent co-pyrolysis research.

Piezoelectric Impedance-based Structural Damage Identification Empowered with Tunable Circuitry Integration and Multi-objective Optimization based Inverse Analysis

Abstract The piezoelectric impedance-based technique is increasingly recognized for its promise in structural health monitoring and damage identification. Built upon their self-sensing actuation capability, piezoelectric transducers can be integrated to host structures to acquire the system-level impedance information in high frequency range with small wavelength. Furthermore, the frequency-sweeping harmonic excitations in impedance measurements lead to the potential for model-based inverse identification of damage location and severity. A major challenge in damage identification, however, is that the inverse analysis is generally underdetermined, as the measurement information may not be adequate to yield a unique solution. In this research, a new methodology of tunable sensing in conjunction with multi-objective optimization inverse analysis is established. Taking advantage of the two-way electromechanical coupling of piezoelectric transducers, tunable inductance is integrated into the measurement circuit. For the same damage scenario, by tuning the inductance to a series of values, a family of impedance measurements can be acquired. Meanwhile, the inverse analysis is cast into a multi-objective optimization problem, aiming at minimizing the difference between measurement and model prediction and achieving sparsity in damage index vector. A Q-learning based multi-objective particle swarm optimization is synthesized to reach a small yet diverse solution set. We report the circuitry integration details as well as the algorithm enhancement with systematic case investigations. It is validated that the new methodology with enriched measurement can produce smaller solution set encompassing the true damage scenario, thereby providing vital information for diagnoses and prognosis.

The scope of alum coagulation-flocculation assisted by slaked lime for the treatment of industrial wastewater containing highly concentrated Acid Black 194 dye. Optimization, molecular weight distribution and toxicity analysis

This work evaluated the capacity of the alum coagulation-flocculation (C–F) process, assisted by slaked lime, for the treatment of industrial textile wastewater (ITWW) containing highly concentrated Acid Black 194 dye. By optimizing the operating conditions ([Alum] = 16.09 g/L, [Ca(OH)2] = 5.16 g/L) through experimental design, response surface methodology, and multi-objective optimization analysis, the C–F process removed 95 % of the dye, 63 % of the COD, 60 % of the TOC, and 94 % of the total Cr concentration, with a total operating cost of 5.99 USD/m3. This improved the biodegradability index (from 0.20 to 0.26), the COS parameter (from 0.14 to 2.46), and the toxicity of the effluent (LC50 for Artemia salina increased from 15.16 mg/L to 56.7 mg/L). The calcium hardness and total hardness were also reduced by 40 % and 44 %, respectively. The C–F supernatant was composed mainly of molecules with molecular weights less than and equal to 10 kDa. An LC-MS spectral analysis revealed the presence of four residual aromatic and recalcitrant compounds in the C–F supernatant, indicating that its further treatment is required before it can be safely discharged or recycled for industrial purposes. The alum C–F process, assisted by slaked lime, demonstrated greater performance, surpassing the individual processes in effectiveness and efficiency, and confirming their synergetic effect. The alum process alone (16.09 g/L) achieved a 37 % reduction COD and 91 % color removal, at a total cost of 4.49 USD/m3. The lime process alone (5.16 g/L) achieved a 37 % COD reduction and 64 % color removal, at a total cost of 1.36 USD/m3.

Does Out-Migration Really Affect Forestry Ecological Security? An Empirical Case Study Based on Heilongjiang Province, China

In the new era, coordinating the relationship between population flow and forestry ecological security has become an important challenge. In this study, we take Heilongjiang Province in China as an example, and through the combination of macro data and micro in-depth interviews, we explore whether population mobility really affects the intrinsic mechanism of forestry ecological security from the perspective of population exodus from forest areas. Based on the DPSIR model, we constructed a forestry ecological security evaluation index system, used the TOPSIS multi-objective decision analysis method to quantify the forestry ecological security status from 2000 to 2022, and utilized the impulse response function of the VAR model to explore the dynamic response relationship between population outflow and forestry ecological security. The results of this study show that, firstly, the comprehensive index of forestry ecological security level in Heilongjiang Province exhibits a fluctuating upward trend from 2000 to 2022. Second, forestry ecological security has a lagged effect on population outflow, and population outflow has almost no effect on forestry ecological security at present. Third, while the population outflow of Luobei County reduces the interference of human activities on the natural environment, it also brings about the pressures of insufficient forestry ecological resource management and forestry personnel. The Dongfanghong Forestry Bureau has effectively improved the efficiency of ecological construction and ecological security through the introduction of digitalized and intelligent equipment, which has effectively compensated for the negative impact of population outflow on the reduction in ecological management personnel. These findings will help realize the coordinated development of population, economy and society, and ecology.

Operational Cost Budget Optimization Analysis to Improve Efficiency of PT Citra Robin Sarana

The aim of the study is to assess the company's ability to optimize the operating cost estimates with the data for the period 2021-2023. A quantitative approach with multi-objective analysis is used, collecting data through interviews and analysis of administrative and financial data. The data used are secondary data from annual financial reports and data of operational costs as well as financial production data of the company. The results of the research showed the difference between the cost of budget and the costs of operational realization of the company: Rs. -161.525.511 in 2021, Rs. 1.530.471.758 in 2022, and Rs. 706.742.283 in 2023. Despite an increase from 2022 to 2023, operating costs are still not optimal. Efforts to optimize risk management have been seen, but have not achieved the expected results.

Multi-objective optimization analysis of construction management site layout based on improved genetic algorithm

In construction management, the rationality of on-site layout is crucial for project progress, cost, and safety. In order to improve the rationality of on-site layout, a multi-objective optimization model combining ant colony algorithm and Pareto optimal solution was constructed based on genetic algorithm, and this model was applied to practical engineering cases. The results show that in terms of computational time, the genetic algorithm takes an average of 1702.0 s, while the improved algorithm takes an average of 421.0 s, which is 1281s less and 85.9% more than before the improvement. The performance of the improved algorithm is the best, and the optimal solution can be obtained through multiple iterations. The improved algorithm has improved the efficiency of on-site layout optimization, and possesses practical application value for the layout of construction management sites. It offers a certain reference for the reasonable setting of construction management sites.