Project Duration Research Articles

The need for carbon finance schemes to tackle overexploitation of tropical forest wildlife.

Defaunation of tropical forests, particularly from unsustainable hunting, has diminished populations of key seed dispersers for many tree species, driving shifts in forest community composition toward small-fruited or wind-dispersed trees with low wood density. Such shifts can reduce aboveground biomass, prompting calls for overexploitation to be included in bioeconomic policy, but a synthesis of existing literature for wildlife impacts on carbon stores is lacking. We evaluated the role of wildlife in tropical forest tree recruitment and found that it was critical to tropical forest carbon dynamics. The emerging financial value of ecosystem services provided by tropical forest fauna highlights the need for carbon-based payments for ecosystem services schemes to include wildlife protection. We argue for three cost-effective actions within carbon finance schemes that can facilitate wildlife protection: support land security opportunities for Indigenous peoples and local communities; provide support for local people to protect forest fauna from overexploitation; and focus on natural regeneration in restoration projects. Incorporating defaunation in carbon-financing schemes more broadly requires an increased duration of carbon projects and an improved understanding of defaunation impacts on carbon stores and ecosystem-level models. Without urgent action to halt wildlife losses and prevent empty forest syndrome, the crucial role of tropical forests in tackling climate change may be in jeopardy.

Schedule Acceleration Planning in Construction Project (Case Study: Japek II Selatan Tollroad)

This study focuses on the remaining work at stations (STA) 53+950 to 54+550 in Jakarta – CikampekII Selatan Toll Road Package III project to find the critical path, completion durations, and projectcosts before and after acceleration. The CPM Technique was used to compile network planningdiagrams, determine the critical path and project duration. Crashing Project was applied to crashschedule through the total project duration and cost whose changes due to the implementation of thecrashing method. The critical path was found in mobilization, tree felling, clearing, dig and haul,borrow material, subgrade preparation, base course (A grade), subbase course (B grade), drainagelayer, lean concrete (t=10 cm), concrete pavement, concrete barrier type B, and signage. In the CPM,the duration of project completion is 214 days. The total project budget under normal circumstancesis Rp22.073.412.654. After accelerating with the Crashing Project by proposed overtime was obtainedthe reduced total cost of Rp32.004.882 from the total normal cost of Rp22.073.412.654 toRp22.041.407.771 with an optimum duration of 183 days

Proposal and appraisal of novel indicators and performance metrics for project monitoring

Project monitoring is a critical process for guaranteeing its excellence, ensuring the delivery of activities both on planned time and budget. Despite its importance, numerous discussions have arisen regarding which indicator is the most appropriate, and how and when to measure it. The primary objective of this article is to evaluate novel duration indicators based on the Earned Value Management (EVM), Earned Schedule Management (ESM) and Earned Duration Management (EDM) methodologies, and to assess new performance metrics. This involves the determination of the limits of the control charts through extensive Monte Carlo Simulations and their performance under several out-of-control scenarios. The robustness of the control charts is tested using performance metrics within a statistical control approach. The secondary objective consists of evaluating the impact of new probability distributions that describe the project activities, such as the Gamma and Conway–Maxwell–Poisson (CMP) distributions. The contributions of this study are threefold: firstly, the Duration Performance Index (DPI) from the EDM is a reliable indicator for monitoring the overall project duration. Secondly, the probability of overreaction (PO) and the accuracy (Ac) are the performance metrics that most produced robust indicators. Lastly, it is observed that different probability distributions, specifically Gamma and Conway–Maxwell–Poison (CMP) distributions, seem to interfere with the correct generation of control charts signals, although they do not significantly affect the overall duration of the entire project.

Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development

Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfactory outcomes. Although existing research has focused on task scheduling, developer recommendations, and reward mechanisms, there has been insufficient attention to the support of platform moderators, or copilots, who are essential to project success. A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. This paper introduces an innovative machine learning approach designed to automate the prediction of project duration on CSD platforms. Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. Zero-shot learning algorithms exhibit superior performance, with an average accuracy of 92.76%, precision of 92.76%, recall of 99.33%, and an f-measure of 95.93%. The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction.

Nanoscale Understanding on CO2 Diffusion and Adsorption in Clay Matrix Nanopores: Implications for Carbon Geosequestration.

Carbon capture and storage (CCS) in subsurface reservoirs represents a highly promising and viable strategy for mitigating global carbon emissions. In the context of CCS implementation, it is particularly crucial to understand the complex molecular diffusive and adsorptive behaviors of anthropogenic carbon dioxide (CO2) in the subsurface at the nanoscale. Yet, conventional molecular models typically represent only single-slit pores and overlook the complexity of interconnected nanopores. In this work, finite kaolinite lamellar assemblages with abundant nanopores (r < 2 nm) were used. Molecular dynamics simulations were performed to quantify the spatial distribution correlations, adsorption preference, diffusivity, and residence time of the CO2 molecules in kaolinite nanopores. The movement of the CO2 molecules primarily occurs in the central and proximity regions of the siloxane surfaces, progressing from larger to smaller nanopores. CO2 prefers smaller nanopores over larger ones. The diffusion coefficients increase, while residence times decrease, with the pore size increasing, differing from typical slit-pore models due to the pore shape and interconnectivity. The perspectives in this study, which would be challenging in conventional slit-pore models, will facilitate our comprehension of the CO2 molecular behaviors in the complex subsurface clay sediments for developing quantitative estimation techniques throughout the CCS project durations.

The Use of Participatory Modelling Methods in Agri-Environmental Research – A Systematic Mapping Dataset

Participatory modelling (PM) is a transdisciplinary research approach that involves stakeholder in a modelling process to develop or improve qualitative or quantitative models. To better understand the potential uses of PM in the emerging field of agroecology living lab research, I conducted a systematic search of the peer-reviewed literature and describe 78 participatory agri-environmental case studies in this dataset. Bibliographic data are included and each case study is described in terms of main goal(s), PM method(s) used, involved stakeholders and their contributions and the level of stakeholder participation in the project. I also extracted key metadata (if available), such as publication type and year, study location (country), funding source, project duration and outcomes beyond publications. This dataset adds value by revealing clusters and associations of methods and goals, by showing the dominating role of researchers in this type of participatory research, by pointing out the absence of PM in certain areas of agricultural production, such as hydroponic farming or viticulture and by providing a comprehensive foundation for the related research article. It also contained data not used in the related research article that could provide a basis for future research, such as linking methods, goals and stakeholder involvement with other metadata or comparing this agri-environmental research and other areas.

Organizational models for the decommissioning of nuclear power plants: Lessons from the United Kingdom and the United States

With nuclear reactor fleets continuously aging, the decommissioning of closed reactors is gaining increasing attention. In nuclear decommissioning, technical, organizational, and regulatory challenges lead to long project durations and cost escalations. This paper attempts to examine the organizational efficiencies in nuclear decommissioning by applying the framework of the "system good" analysis and assessments based on new institutional economics. It compares the former parent-body-organization model practiced in the United Kingdom with current approaches observed in the United States, highlighting potential gains and policy recommendations for private sector involvement in nuclear decommissioning. Inefficiencies experienced in the United Kingdom include regulatory issues, private actors' opportunism, and information asymmetries. Comparatively, the models observed in the United States show fewer such issues, but regulators should address potential profit-driven shortcuts and funding adequacy.

Developing a Robust Multi-Skill, Multi-Mode Resource-Constrained Project Scheduling Model with Partial Preemption, Resource Leveling, and Time Windows

Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for limited access to renewable resources. This research presents a scheduling model with constrained multi-skill and multi-mode resources, where activity durations vary under different scenarios and allow for partial pre-emption due to resource shortages. The main innovation is the pre-emption of activities when resources are unavailable, with defined minimum and maximum delivery time windows. For this purpose, a multi-objective mathematical programming model is developed that considers Bertsimas and Sim’s robust model in uncertain conditions. The model aims to minimize resource consumption, idleness, and project duration. The proposed model was solved using a multi-objective genetic algorithm and finally, its validation was completed and confirmed. Analysis shows that limited renewable resources can lead to increased activity pre-emption and extended project timelines. Additionally, higher demand raises resource consumption, reducing availability and prolonging project duration. Increasing the upper time window extends project time while decreasing the lower bound pressures resources, leading to higher consumption and resource scarcity.

Multimodal Hong Kong: Documenting soundscape and smellscape at places of intangible cultural heritage

The sensory cultural heritage creates identity and cohesion in a community. The Multimodal Hong Kong (MMHK) started in January 2023 and aims to build a database of soundscape and smellscape at culturally significant sites. This paper will introduce the project and its objectives, present results from a pilot survey, and describe the development of a field work methodology. Data collection methods include Ambisonics audio, 360 video, and 'smellprints', together with on-site observations based on the Soundscape Indices survey protocol and the Dravnieks-McGinley-Zarzo 'smell atlas'. Currently having data from 20 sites, MMHK aims to reach 100 within the project's duration, as well as conduct outreach such as sensory walks, interviews, and workshops with stakeholders. The overall goal is to produce in-depth knowledge of how information, via multiple sensory modalities, constitute the perceived quality of places, and thence, how an intangible cultural heritage emerges in Hong Kong. This knowledge will enable future research and applications in urban spatial design, experiencescape, and virtual tourism.

FOBICS: Assessing project security level through a metrics framework that evaluates DevSecOps performance

Context:In today’s software development landscape, the DevSecOps approach has gained traction due to its focus on the software development process and bolstering security measures in projects, a task in light of the ever-evolving cybersecurity threats. Objective:This study aims to address the lack of metrics for quantitatively assessing its efficacy from both security and business logic perspectives. Methods:To tackle this issue, the research introduces the Framework of Business Index Concerning Security (FOBICS), a set of metrics designed to enable transparent evaluations of project security. FOBICS considers various perspectives relevant to DevSecOps practices. It includes factors such as project duration and financial outcomes, making it appealing for implementation in business settings. Results:The effectiveness of FOBICS is validated theoretically and empirically via its application in two real-world projects: the results from these implementations show a correlation between FOBICS metrics and the security strategies employed as the development methodologies adopted by diverse teams throughout the projects. Conclusion:Hence, FOBICS emerges as a tool for assessing and continuously monitoring project security, offering insights into areas of strength and areas that may require enhancement. FOBICS is shown to be effective in assessing the level of DevSecOps implementation. The ease of calculating FOBICS metrics makes them easily interpretable and continuously verifiable. Moreover, FOBICS summarizes most of the other quantitative and qualitative metrics in the literature.

Time Management with Quality Control in Construction Projects by Critical Chain Project Management

The construction industry consistently grapples with challenges related to project delays, budget overruns and quality control, particularly in the residential sector. Critical Chain Project Management (CCPM) emerges as a promising approach to address these issues by integrating resources availability with project scheduling. This literature review explores the application of CCPM in residential construction, highlighting its effectiveness in improving time management and project out comes. By analysing existing research, case studies and practical examples the findings demonstrate that implementing CCPM can lead to significant reductions in project duration and enhancements in resource utilization. The review aims to provide insights into the unexplored areas of CCPM in scheduling, ultimately emphasizing its potential to transform time management practices in the construction industry.

Stability analysis of deep foundation pit with a double-row cast-in-place piles and diagonal steel lattice braces under sloped excavation conditions

Existing deep foundation pit support structures are commonly composed of external earth-retaining structures, internal horizontal bracings, and vertical columns. A closed bracing system, often formed by a horizontal support through a bracket board, frequently impedes vertical excavation and soil removal operations in the foundation pit, and the processes of assembly and dismantling are complex and time-consuming. This study presents a combined support system and construction method consisting of cast-in-place piles and diagonal steel lattice braces. For sloped excavation, diagonal braces were constructed by slotting through the reserved soil, allowing the use of a single layer of support within the excavation depth. This approach significantly optimizes the construction process, reducing both project duration and overall cost. The field monitoring results indicated that the support method effectively controlled the lateral displacement of the pile bodies. Field monitoring results demonstrated that the proposed support system effectively controlled the lateral displacement of the pile bodies. The adoption of a support-first, excavation-second approach significantly controlled the settlement of the ground surface around the foundation pit, thereby preventing excessive increments in the axial force of the supports due to the large longitudinal depth excavation. The calculation results of the three-dimensional finite element model for foundation pit excavation and support indicate that the proposed support method results in a decreasing ratio of the maximum lateral deformation depth of the pile body, denoted as δh-m, to the excavation depth He as the excavation depth increases. This implied that the displacement of the pile body was strictly controlled. When the depth of the foundation pit excavation exceeded 10 m, the maximum lateral deformation occurred below 10 m along the pile shaft. The diagonal steel lattice braces transferred the load to the top of the cast-in-place piles at the bottom of the pit, where the stress concentration occurred. During construction, special attention must be paid to the strength of the connection between the pile top and the connecting beams.

Project Acceleration Analysis Using the Crashing Method with The Alternative of Additional Labor and Additional Working Hours in Drainage Work

Project work delays often occur due to differences in site conditions, design changes, weather influences, and planning errors. The problem faced is the project duration, where if the project duration is accelerated or crashes, there will be several options to cut the project duration. This is the problem that becomes the topic of the thesis/journal, namely "Analysis of Project Acceleration with the Crashing Method with Alternatives for Adding Manpower and Additional Working Hours in Drainage Works". The author uses the primary method, with sources of RAB, Time Schedule, working drawings and analysis of city unit prices. As well as secondary research methods with sources, from relevant books, and project data such as price lists and wages. From the results of the study it can be concluded that the total wages of workers under normal conditions is IDR. 16,864,000.00 with the duration of project work for 30 working days. From the results of the analysis in this study, the total wages of workers with alternative labor additions of IDR. 19,672,000.00 with the duration of project construction work for 25 working days or 15.15% faster than the normal duration. Meanwhile, the total wage for workers with the alternative of additional working hours (overtime) is IDR. 25,052,300.00 with the duration of the project construction work for 25 working days or 15.15% faster than the normal duration. A more economical alternative to completing the project is the addition of manpower because it saves IDR. 2,228,000.00 from additional working hours (overtime), from normal wages.

Evaluating the Impact of Artificial Intelligence in Managing Construction Engineering Projects

This study evaluates the impact of utilizing artificial intelligence (AI) in managing construction engineering projects. With the increasing complexity and scale of construction projects, AI offers promising solutions to enhance efficiency, accuracy, and decision-making processes. The study investigates the potential benefits, challenges, and practical applications of AI through detailed case studies. The study employs a mixed-methods approach, combining qualitative and quantitative research methods. Data was collected through literature reviews and case studies where AI had been successfully implemented. The analyses included comparisons between projects that used AI and those that did not. The findings demonstrated significant improvements in project efficiency, cost estimation accuracy, and risk management. For instance, AI-powered systems reduced scheduling errors by 35%, leading to more accurate timelines. Additionally, the integration led to a 20% reduction in project durations due to improved resource allocation and proactive risk management. Furthermore, AI-supported systems contributed to a 25% improvement in stakeholder satisfaction. In terms of cost estimation, AI-powered estimation tools improved the accuracy of cost estimates by 30%, helping to reduce budget overruns by 40%. In risk management, AI-supported tools enhanced the accuracy of risk identification by 45%, leading to the early detection of potential issues and the development of effective mitigation strategies that reduced the impact of risks by 30%. Thanks to these improvements, project success rates increased by 20%. These results demonstrate that integrating AI into the management of engineering construction projects can lead to tangible improvements in project efficiency, cost accuracy, and risk management, thereby enhancing stakeholder satisfaction and contributing to more successful project outcomes.

A data-driven approach for predicting cash flow performance of public owners in building projects: insights from Turkish cases

PurposeThe cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the project performance of contractors and lead to conflicts between two parties in the Turkish construction industry. Although some previous studies focused on the issues in internal cash flows (e.g. inflows and outflows) of construction companies, the context of cash flows from public agencies to contractors in public projects is still unclear. Therefore, the primary objective of this study is to develop and test diverse machine learning-based predictive models on the progress payment performance of Turkish public agencies and improve the predictive performance of these models with two different optimization algorithms (e.g. first-order and second-order). In addition, this study explored the attributes that make the most significant contribution to predicting the payment performance of Turkish public agencies.Design/methodology/approachIn total, project information of 2,319 building projects tendered by the Turkish public agencies was collected. Six different machine learning algorithms were developed and two different optimization methods were applied to achieve the best machine learning (ML) model for Turkish public agencies' cash flow performance in this study. The current research tested the effectiveness of each optimization algorithm for each ML model developed. In addition, the effect size achieved in the ML models was evaluated and ranked for each attribute, so that it is possible to observe which attributes make significant contributions to predicting the cash flow performance of Turkish public agencies.FindingsThe results show that the attributes “inflation rate” (F5; 11.2%), “consumer price index” (F6; 10.55%) and “total project duration” (T1; 10.9%) are the most significant factors affecting the progress payment performance of government agencies. While decision tree (DT) shows the best performance among ML models before optimization process, the prediction performance of models support vector machine (SVM) and genetic algorithm (GA) has been significantly improved by Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based Quasi-Newton optimization algorithm by 14.3% and 18.65%, respectively, based on accuracy, AUROC (Area Under the Receiver Operating Characteristics) and F1 values.Practical implicationsThe most effective ML model can be used and integrated into proactive systems in real Turkish public construction projects, which provides management of cash flow issues from public agencies to contractors and reduces conflicts between two parties.Originality/valueThe development and comparison of various predictive ML models on the progress payment performance of Turkish public owners in construction projects will be the first empirical attempt in the body of knowledge. This study has been carried out by using a high number of project information with diverse 27 attributes, which distinguishes this study in the body of knowledge. For the optimization process, a new hyper parameter tuning strategy, the Bayesian technique, was adopted for two different optimization methods. Thus, it is available to find the best predictive model to be integrated into real proactive systems in forecasting the cash flow performance of Turkish public agencies in public works projects. This study will also make novel contributions to the body of knowledge in understanding the key parameters that have a negative impact on the payment progress of public agencies.

Towards a national model of humus (carbon) budget management in soils of agroecosystems in the Russian Federation

The prospect of adapting two methods for monitoring the fertility of agricultural soils (humus balance, HB) under spring and winter wheat crops (hereinafter referred to as grain crops) developed in the Russian Federation (RF) for the purposes of climate projects has been studied. It was revealed that the RosNIIzemproekt method (1998) underestimates the HB average value to -7.7 c/ha for RF. The RosNIIzemproekt method (1998) has logical errors in the calculation model and is not recommended for use. The TsINAO method (2000) has a more logical calculation model. According to this method, the average value of HB in soils for RF is generally positive and amounts to 3.3 c/ha (0.71 tCO2-eq/ha carbon units). This value constitutes ≈0.3% of average humus reserves in arable soils of the RF (108 tС/ha). The annual value of change in humus content under spring and winter wheat is incommensurably less than the total humus reserves in the soil. Statistically reliable assessment of changes in humus content requires a huge number of soil samples. This makes the verification procedure economically inexpedient. Another option is proposed to increase the time of humus accumulation. It is considered (IPCC, 2003) that a 20-year accumulation period is sufficient, which corresponds to the duration of soil climate projects. A coefficient (0.216) has been proposed for converting humus reserves (c/ha) to the value of a carbon unit (tCO2-eq/ha). The total sequestration of carbon by soils under grain crops in 2022 was about 11 million 914 thousand tCO2-eq. HB accounting significantly compensates for calculated emissions from agricultural production and reduces them by almost 10% (from 116 to 105 million tons CO2-eq in 2022). For both models of fertility monitoring, an assessment was made of the quality of the regression dependence of HB on the total biomass of photosynthesis (TBP) in soils under grain crops. It is shown that according to Fisher's t-test at a 5% significance level, the studied relationship is significant and characterized by the squares of the correlation coefficient (R2) equal to 0.554 and 0.998. In accordance with the Chaddock scale, the closeness of the correlative relationship between HB and TPB for winter wheat is assessed as high, and for spring wheat as very high.

Feasibility study of floating solar photovoltaic systems using techno-economic assessment and multi-criteria decision-making method: A case study of Bangladesh

Many developing countries with abundant solar resources and rich history of agriculture, face overpopulation and land shortage issues. Floating solar photovoltaics (FSPVs) do not compete with agriculture for land and offer higher energy potential than land-based photovoltaics (LBPVs). For feasibility and site suitability evaluation of FSPVs, this study proposes an integrated approach of techno-economic assessment (TEA) and multi-criteria decision making (MCDM), based on a case study of Bangladesh. The system advisor model (SAM) was used to conduct TEA, evaluating the potential of nine FSPV sites on a regional scale using ten criteria. Combined compromise solution (CoCoSo) was implemented to determine the optimum site. Bergobindopur lake was ranked top, with the highest energy yield and lowest cost of energy of 1564 kWh/kW and $0.055/kWh respectively. Case study of Bergobindopur revealed buy-all-sell-all electricity tariff was more profitable than net billing and net metering. Energy generation was found to be influenced by tilt angle and soiling. Sensitivity analyses demonstrated the superiority of FSPVs over LBPVs for varying electricity prices, degradation and discount rates, project durations, and investor's equities. Longer project durations and lower equities resulted in higher profits for investors. The proposed approach contributes to the expansion of FSPV in land-constrained areas.

Assessing cost and cost savings of teleconsultation in long-term care facilities: a time-driven activity-based costing analysis within a value-based healthcare framework

BackgroundQuebec’s healthcare system faces significant challenges due to labour shortage, particularly in long-term care facilities (CHSLDs). The aging population and increasing demand for services compound this issue. Teleconsultation presents a promising solution to mitigate labour shortage, especially in small CHSLDs outside urban centers. This study aims to evaluate the cost and cost savings associated with teleconsultation in CHSLDs, utilizing the Time-Driven Activity-Based Costing (TDABC) model within the framework of Value-Based Healthcare (VBHC).MethodsThis study focuses on CHSLDs with fewer than 50 beds in remote regions of Quebec, where teleconsultation for nighttime nursing care was implemented. Time and cost data were collected from three CHSLDs over varying periods. The TDABC model, aligned with VBHC principles, was applied through five steps, including process mapping, estimating activity times, calculating resource costs, and determining total costs.ResultsTeleconsultation increased the cost per minute for nursing care compared to traditional care, attributed to additional tasks during remote consultations and potential technical challenges. However, cost savings were realized due to reduced need for onsite nursing staff during non-eventful nights. Overall, substantial savings were observed over the project duration, aligning with VBHC’s focus on delivering high-value healthcare.ConclusionsThis study contributes both theoretically and practically by demonstrating the application of TDABC within the VBHC framework in CHSLDs. The findings support the cost savings from the use of teleconsultation in small CHSLDs. Further research should explore the long-term sustainability and scalability of teleconsultation across different CHSLD sizes and settings within the VBHC context to ensure high-value healthcare delivery.

Future Shorelines: A Living Shoreline Site Selection and Design Decision Support Tool that Incorporates Future Conditions Induced by Sea Level Rise

Most living shoreline site selection and design decision support tools are based upon existing environmental conditions. We developed a web-based, geospatial tool called Future Shorelines that integrates high-resolution landscape elevation data and a matrix of locally derived NOAA Interagency Sea Level Rise Scenarios to characterize future conditions of submergence and shoreline translation induced by sea level rise. Once the practitioner selects a location of interest, sea level rise scenario (e.g., high), and target year (e.g., 2050), the tool will generate plan view and cross-sectional informational graphics specific to their choices. This information can then be paired with other menu options, like parcel ownership, to facilitate the planning and construction of nature-based shoreline stabilization solutions that (1) are located where opportunities for horizontal migration are optimized, (2) remain accessible for monitoring and maintenance, and (3) perform as intended over the design life of the installation. The tool’s menu options and the user interface were informed by project partner input solicited during numerous workshops convened over the duration of the 2-year project. This coproduction created a product that was familiar to the end user and therefore increased the likelihood that it would be utilized by them during the planning and design of living shoreline projects. Although developed for use in the Indian River Lagoon, located along the east-central Florida coast, it can be seamlessly replicated for application in other coastal regions of the USA where the requisite data are available.

Safety Management System in Pune Metro Rail Projects

The construction industry continues to grow as the demand for infrastructure, homes, and officespaces grows by the day. Since the construction industry is so dynamic, it is vulnerable to a range ofhealth risks. As a result, protection is a top priority in the construction sector to ensure a healthy workingenvironment. Safety experts have determined that risky behaviours cause the majority of workplaceinjuries, and that controlling these behaviours is one of the keys to effective accident prevention and a lowaccident rate on construction sites. Safety in construction industry is much more important. This reviewpaper is about to increase safety in the building industry performance. The study's primarily is to definethe critical success factors that affect the implementation of construction safety management. Thisreview paper is to recognize and solve major problems in the infrastructure sector with varioussolutions related to work situations that affect project safety performance. Due to lake of knowledge andawareness large number of people’s deaths and long-term injuries occurs. in order to reduce andcontrol construction worker health and safety.The construction industry has the largest number of injuries compared to other industries. Thus,reducing accidents and determining construction risks are extremely important. One of the essentialsteps for construction safety management is hazard identification, since the most unmanageable risksare from unidentified hazardsPune metro project is the key project for city the accident at going site may causes the break inprogress of work ultimately it casues the increases the duration of project as well as cost of projects.The various safety and control measures of accidents in Pune Metro projects will outlined in thisproject in order to mitigate accidents