title: Experimental investigation of CFS to steel connection using self-drilling screws

authors: Anthony, Anu

abstract: Screws are one of the most used methods for joining structural steel to Cold Formed Steel (CFS). An improved substitute for conventional nail and bolt connections is self-drilling screws. The wings in the screws make it easier to drive the screw into the steel without the use of a pre-drilled hole. Given that joints are the primary means of transferring energy to a structure, it is especially crucial to comprehend the seismic behaviour of their connection. New Zealand, for example, is a country that primarily experiences seismic activity and builds its buildings using a combination of structural steel and CFS. Numerous research that looks into how structural joints behave under cyclic and monotonic loading conditions are available in the literature. However, the majority of these studies did not address the use of self-drilling screws in cyclic stress to link CFS to structural steel. Thus, by experimental testing, this research examines the load deformation and failure mechanisms of CFS and steel connections utilising screws under cyclic and monotonic loads. 
Tensile testing was done to evaluate the material's properties of steel. A total of 54 experimental investigations were conducted to determine the cyclic nature of the junction. The structural steel thickness (8mm, 12mm, 16mm, and 20mm), the CFS thickness (1.8mm), and the screw diameter (6.3mm hexagonal screw), and varying the number of screws (1 and 2) were considered in test program. The same samples were subjected to monotonic tests in order to determine the maximum and minimum displacement for the cyclic loading. A total of 24 tests were performed in order to acquire the values. The experiment's findings, including load deformation and the failure process, are identified. Based on the contrasting data, the cyclic nature of all the connections can be analysed to ascertain which is the most correct.
The New Zealand standard AS/NZS 4600:2018, which particularly addresses the screw spacing with each other and the distance from the screw to the edge of the specimen, will serve as the basis for both the assembly and the spacing of the structural joints. The loading procedures and the number of cycles employed for cyclic loading are detailed in the Federal Emergency Management Agency 461 (FEMA). Appropriate displacement amplitudes for cyclic tests were determined using the initial results from the monotonic tests. 
During the cyclic testing it could be found that most of the specimens failed due to bearing and tear out failure. There is an average increase of 56.77 % in capacity as the number of screws were increased from single screw to double screw connection.  As the steel thickness was increased as 8mm, 12mm, 16mm, and 20mm, the average capacity was reduced to 10.13%, 18.11%, 7.98% accordingly for a single screw connection. For double screw connection as the steel thickness was increased as 8mm,12mm, 16mm, and 20mm, the average capacity was increased to 0.79%, 4.91%, 2.15% accordingly.

text: Experimental investigation of CFS to steel connection using self-drilling screws Screws are one of the most used methods for joining structural steel to Cold Formed Steel (CFS). An improved substitute for conventional nail and bolt connections is self-drilling screws. The wings in the screws make it easier to drive the screw into the steel without the use of a pre-drilled hole. Given that joints are the primary means of transferring energy to a structure, it is especially crucial to comprehend the seismic behaviour of their connection. New Zealand, for example, is a country that primarily experiences seismic activity and builds its buildings using a combination of structural steel and CFS. Numerous research that looks into how structural joints behave under cyclic and monotonic loading conditions are available in the literature. However, the majority of these studies did not address the use of self-drilling screws in cyclic stress to link CFS to structural steel. Thus, by experimental testing, this research examines the load deformation and failure mechanisms of CFS and steel connections utilising screws under cyclic and monotonic loads. 
Tensile testing was done to evaluate the material's properties of steel. A total of 54 experimental investigations were conducted to determine the cyclic nature of the junction. The structural steel thickness (8mm, 12mm, 16mm, and 20mm), the CFS thickness (1.8mm), and the screw diameter (6.3mm hexagonal screw), and varying the number of screws (1 and 2) were considered in test program. The same samples were subjected to monotonic tests in order to determine the maximum and minimum displacement for the cyclic loading. A total of 24 tests were performed in order to acquire the values. The experiment's findings, including load deformation and the failure process, are identified. Based on the contrasting data, the cyclic nature of all the connections can be analysed to ascertain which is the most correct.
The New Zealand standard AS/NZS 4600:2018, which particularly addresses the screw spacing with each other and the distance from the screw to the edge of the specimen, will serve as the basis for both the assembly and the spacing of the structural joints. The loading procedures and the number of cycles employed for cyclic loading are detailed in the Federal Emergency Management Agency 461 (FEMA). Appropriate displacement amplitudes for cyclic tests were determined using the initial results from the monotonic tests. 
During the cyclic testing it could be found that most of the specimens failed due to bearing and tear out failure. There is an average increase of 56.77 % in capacity as the number of screws were increased from single screw to double screw connection.  As the steel thickness was increased as 8mm, 12mm, 16mm, and 20mm, the average capacity was reduced to 10.13%, 18.11%, 7.98% accordingly for a single screw connection. For double screw connection as the steel thickness was increased as 8mm,12mm, 16mm, and 20mm, the average capacity was increased to 0.79%, 4.91%, 2.15% accordingly.

year: 2025-02-22

title: Remote sensing of dynamics and aboveground biomass of seagrass in Tauranga Harbor (Bay of Plenty), New Zealand

authors: Ha, Nam-Thang

subjects: Seagrass

abstract: Seagrasses are angiosperm plants that are completely adapted to life in seawater. They are distributed widely across the climatic regions, ranging from the tropics to temperate regions, in both inter-tidal and sub-tidal zones. Multiple ecosystem services are recognized as being supported by seagrass meadows, which recently has included appreciation role in carbon sequestration. Seagrass meadows, however, have been degraded in both terms of area and habitat quality across the globe, leading to a significant loss of ecosystem services and human livelihood support. This ongoing degradation has resulted in an urgent need to develop tools for assessing the temporal changes of extant meadows and accurate estimation of seagrass biological parameters, which will contribute to a sustainable conservation strategy into the future. 

This thesis describes the use of a range of freely available Earth observation products, including multi-spectral imagery from Landsat and Sentinel-2, and synthetic aperture radar (SAR) products from Sentinel-1, coupled with a range of machine learning (ML) and meta-heuristic optimization algorithms, to develop novel and advanced techniques for remote sensing of seagrass. The work used field validation data from Tauranga Harbor, New Zealand, and specifically targeted mapping, change detection, and estimation of seagrass distribution and biomass. 

The relatively small and patchy meadows of Zostera muelleri in this harbor can be mapped using a three-category classification (dense, sparse and none) with up to 91% accuracy for dense and 75% for sparse meadows using the machine learning algorithm Rotation Forest with Sentinel-2 imagery. Despite a slightly lower accuracy (90%), the algorithm Canonical Correlation Forest also shows merit for categorical seagrass mapping. Historic Landsat multispectral satellite data used with ML models was able to map accurately the change in distribution of seagrass meadows over 29 years (1989 - 2019). For this binary mapping application (presence/absence) the CatBoost model obtained over 90% accuracy. 

Historic imagery indicates an approximately 50% of seagrass loss, from 2,424 ha in the year 1989 down to 1,184 ha in the year 2019 in Tauranga Harbor. Most of the early loss was from the northern and southern parts of the harbor and results were consistent with published estimates of change based on aerial photography. 

In addition, a mapping scheme of seagrass distribution was developed from SAR data and a fusion of the multi-spectral and SAR data was developed for seagrass aboveground biomass (AGB) estimation. Optimal results were obtained using a combination of ML methods and metaheuristic optimization. The seagrass distribution was mapped at an accuracy over 90% using the Extreme Gradient Boost (XGB) whilst the AGB map at 10 m spatial resolution was produced at 75% accuracy using the XGB model together with Sentinel-2 images and Particle Swarm Optimization (PSO). 

The last part of the thesis describes the development of a web-based application to allow the advances in this research to be shared with a broader community and strengthen international and domestic collaboration in seagrass protection and conservation. 

This study provides in-depth and advanced methods for seagrass resource inventory, maximizing the utilization of remotely sensed data, state-of-the-art ML and metaheuristic optimization algorithms to accurately map distribution and estimate desired biophysical parameters. The proposed methods are open-source and applicable across the globe, providing a complete toolset for both scientist and managers in aquatic resource management.

text: Remote sensing of dynamics and aboveground biomass of seagrass in Tauranga Harbor (Bay of Plenty), New Zealand Seagrasses are angiosperm plants that are completely adapted to life in seawater. They are distributed widely across the climatic regions, ranging from the tropics to temperate regions, in both inter-tidal and sub-tidal zones. Multiple ecosystem services are recognized as being supported by seagrass meadows, which recently has included appreciation role in carbon sequestration. Seagrass meadows, however, have been degraded in both terms of area and habitat quality across the globe, leading to a significant loss of ecosystem services and human livelihood support. This ongoing degradation has resulted in an urgent need to develop tools for assessing the temporal changes of extant meadows and accurate estimation of seagrass biological parameters, which will contribute to a sustainable conservation strategy into the future. 

This thesis describes the use of a range of freely available Earth observation products, including multi-spectral imagery from Landsat and Sentinel-2, and synthetic aperture radar (SAR) products from Sentinel-1, coupled with a range of machine learning (ML) and meta-heuristic optimization algorithms, to develop novel and advanced techniques for remote sensing of seagrass. The work used field validation data from Tauranga Harbor, New Zealand, and specifically targeted mapping, change detection, and estimation of seagrass distribution and biomass. 

The relatively small and patchy meadows of Zostera muelleri in this harbor can be mapped using a three-category classification (dense, sparse and none) with up to 91% accuracy for dense and 75% for sparse meadows using the machine learning algorithm Rotation Forest with Sentinel-2 imagery. Despite a slightly lower accuracy (90%), the algorithm Canonical Correlation Forest also shows merit for categorical seagrass mapping. Historic Landsat multispectral satellite data used with ML models was able to map accurately the change in distribution of seagrass meadows over 29 years (1989 - 2019). For this binary mapping application (presence/absence) the CatBoost model obtained over 90% accuracy. 

Historic imagery indicates an approximately 50% of seagrass loss, from 2,424 ha in the year 1989 down to 1,184 ha in the year 2019 in Tauranga Harbor. Most of the early loss was from the northern and southern parts of the harbor and results were consistent with published estimates of change based on aerial photography. 

In addition, a mapping scheme of seagrass distribution was developed from SAR data and a fusion of the multi-spectral and SAR data was developed for seagrass aboveground biomass (AGB) estimation. Optimal results were obtained using a combination of ML methods and metaheuristic optimization. The seagrass distribution was mapped at an accuracy over 90% using the Extreme Gradient Boost (XGB) whilst the AGB map at 10 m spatial resolution was produced at 75% accuracy using the XGB model together with Sentinel-2 images and Particle Swarm Optimization (PSO). 

The last part of the thesis describes the development of a web-based application to allow the advances in this research to be shared with a broader community and strengthen international and domestic collaboration in seagrass protection and conservation. 

This study provides in-depth and advanced methods for seagrass resource inventory, maximizing the utilization of remotely sensed data, state-of-the-art ML and metaheuristic optimization algorithms to accurately map distribution and estimate desired biophysical parameters. The proposed methods are open-source and applicable across the globe, providing a complete toolset for both scientist and managers in aquatic resource management.

year: 2021

title: Waiwhakareke Restoration Plantings: Establishment of Monitoring Plots 2005-06

authors: Grove, E.

subjects: New Zealand

abstract: Waiwhakareke Natural Heritage Park is being developed to reconstruct native lowland and wetland ecosystems as were once widespread in the Waikato Region. The 60ha Natural Heritage Park is located on the north-west outskirts of Hamilton City and includes a peat lake (Horseshoe Lake) which is surrounded by introduced willow trees. There is some native marginal vegetation around the lake, including rushes and sedges, and an extensive area of gently sloping pasture completes the catchment. The restoration and recreation of the native plant and animal communities is being lead by the Hamilton City Council in partnership with The University of Waikato, Wintec, Nga Mana Toopu o Kirikiriroa Limited Resource Management and Cultural Consultants and Tui 2000 (McQueen 2005; McQueen & Clarkson 2003).

text: Waiwhakareke Restoration Plantings: Establishment of Monitoring Plots 2005-06 Waiwhakareke Natural Heritage Park is being developed to reconstruct native lowland and wetland ecosystems as were once widespread in the Waikato Region. The 60ha Natural Heritage Park is located on the north-west outskirts of Hamilton City and includes a peat lake (Horseshoe Lake) which is surrounded by introduced willow trees. There is some native marginal vegetation around the lake, including rushes and sedges, and an extensive area of gently sloping pasture completes the catchment. The restoration and recreation of the native plant and animal communities is being lead by the Hamilton City Council in partnership with The University of Waikato, Wintec, Nga Mana Toopu o Kirikiriroa Limited Resource Management and Cultural Consultants and Tui 2000 (McQueen 2005; McQueen & Clarkson 2003).

year: 2006